{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[nltk_data] Downloading package punkt to /home/jonas/nltk_data...\n", "[nltk_data] Package punkt is already up-to-date!\n", "[nltk_data] Downloading package averaged_perceptron_tagger to\n", "[nltk_data] /home/jonas/nltk_data...\n", "[nltk_data] Package averaged_perceptron_tagger is already up-to-\n", "[nltk_data] date!\n", "[nltk_data] Downloading package wordnet to /home/jonas/nltk_data...\n", "[nltk_data] Package wordnet is already up-to-date!\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from IPython.display import clear_output, Markdown, Math\n", "import ipywidgets as widgets\n", "import os\n", "import glob\n", "import json\n", "import numpy as np\n", "import itertools\n", "import sklearn.utils as sku\n", "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import MultiLabelBinarizer\n", "import nltk\n", "nltk.download('punkt')\n", "nltk.download('averaged_perceptron_tagger')\n", "nltk.download('wordnet')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.append(\"..\")\n", "\n", "from Tools.Emoji_Distance import sentiment_vector_to_emoji\n", "from Tools.Emoji_Distance import emoji_to_sentiment_vector\n", "\n", "def emoji2sent(emoji_arr):\n", " return np.array([emoji_to_sentiment_vector(e) for e in emoji_arr])\n", "\n", "def sent2emoji(sent_arr, custom_target_emojis=None):\n", " return [sentiment_vector_to_emoji(s, custom_target_emojis=custom_target_emojis) for s in sent_arr]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "SINGLE_LABEL = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# simple twitter approach\n", "*for learning emoji usage by single (in the meaning of unconnected) twitter messages*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## loading train data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Download preprocessed raw data:\n", " * [here](https://the-cake-is-a-lie.net/nextcloud/index.php/s/MmXFYj6mGoMQoJN) for english\n", " * [here](https://the-cake-is-a-lie.net/nextcloud/index.php/s/HgqpQ6rFadtWSAt) for german" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data_root_folder = \"./data_en/\" # i created a symlink here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* get all json files in `data_root_folder`" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "json_files = sorted(glob.glob(data_root_folder + \"/*.json\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* so far, only load the first file" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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EMOJIHASHTAGSLINKED_USERdatetimeidlangpersonreply_totext
0[πŸ”₯, πŸ‘][][]2017-11-01 13:29:00925716304635547600en31507978NaNfashionbombdaily's photo <EMOJI><EMOJI>🏼
1[🀦][][]2017-11-01 13:29:00925716304664911900en231994649NaNIt’s scary how on point my horoscope be <EMOJI...
2[πŸ˜„][][]2017-11-01 13:29:03925716317214089200en2592765104NaNWoooaaaahhh <EMOJI>
3[πŸ“·][][]2017-11-01 13:29:04925716321416949800en278737933NaN<EMOJI> vivalcli: Portraits by Zhao Guojing an...
4[😩, 😩][][@hiphopphiIes]2017-11-01 13:29:06925716329801310200en8245862536349819009.257162e+17<USER> i wanna know too<EMOJI><EMOJI>
5[😭, πŸ’“][][@WizMommma]2017-11-01 13:29:02925716313019965400en15819538149.257088e+17<USER> veda was yoda too <EMOJI><EMOJI>
6[πŸ˜‚][][]2017-11-01 13:29:05925716325607133200en1001999683NaNI’m less stressed about turning 30 now <EMOJI>...
7[πŸ’―][][]2017-11-01 13:29:07925716334008082400en745222369183043600NaNFull charged. <EMOJI>
8[πŸ™„][][@SeaDimon, @lsarsour]2017-11-01 13:29:09925716342401052700en7985571552175391009.257147e+17<USER> That’s part of the problem, (they) <USE...
9[😟, πŸ˜₯, 😒][][@Ian_khetye]2017-11-01 13:29:10925716346570240000en7443960391264215009.250629e+17<USER> got me emotional there<EMOJI><EMOJI><EM...
10[🌻][][]2017-11-01 13:29:13925716359182520300en7214900101182054008.965900e+17back to the yellow <EMOJI>
11[🍁, 🌺, πŸ‚][][@Dimafadma]2017-11-01 13:29:15925716367558545400en5205367239.257159e+17<USER> Happy month to you and your loved ones ...
12[πŸƒ][#mortdale, #partofthefamily, #gorgeousboy][]2017-11-01 13:29:16925716371735900200en850852815941517300NaNMaxx and Patricia. Family hangs at For Good He...
13[πŸ’­, 🀦][][]2017-11-01 13:29:20925716388513230800en914145041588867100NaNI need to STOP beating myself up with my thoug...
14[😍, 😘][#7YearsOfKMH2][]2017-11-01 13:29:20925716388525645800en2425405622NaNCutest Son <EMOJI>Roll no. 31 <EMOJI> <HASHTAG>
15[😜][][]2017-11-01 13:29:22925716396931240000en4614871873NaNBy the summer I should have everything up and ...
16[πŸ˜‚, πŸ”₯][][]2017-11-01 13:29:23925716401125331000en2831608345NaNI know my English is not that good but that do...
17[πŸ’•][][@yungbabytate]2017-11-01 13:29:23925716401133948900en7885719746330092009.255778e+17<USER> I <EMOJI> u mama
18[πŸ˜‰][][@cmckenney]2017-11-01 13:29:23925716401125544000en2183078029.257115e+17<USER> That picture was NOT taken this morning...
19[πŸ‘…][#footfetishnation][]2017-11-01 13:29:25925716409489002500en885261166146179100NaNWelcome to <HASHTAG> <EMOJI>
20[πŸ‘Œ, πŸ™‚][][]2017-11-01 13:29:25925716409497272300en831437760833609700NaNAwkward <EMOJI><EMOJI>
21[πŸ€—, πŸ“Ί][][]2017-11-01 13:29:26925716413699854300en231664542NaNback at it with supernatural <EMOJI><EMOJI>
22[πŸ’―][][]2017-11-01 13:29:26925716413679009800en3196847035NaNOne of the best things I've learned was to sto...
23[πŸ‘…, πŸ’¦, πŸ‘][][@ctrlpurp]2017-11-01 13:29:29925716426278735900en9184928583526359009.257161e+17<USER> Can I taste?<EMOJI><EMOJI><EMOJI>
24[πŸ’”][][@saunders_court1]2017-11-01 13:29:30925716430473039900en34711873379.257163e+17<USER> we miss you ☹️<EMOJI>
25[🀐, 🀐, 🀐][][]2017-11-01 13:29:31925716434667184100en780060488600199200NaNActually my bias in WJSN are Eunseo &amp; Bona...
26[😴][][]2017-11-01 13:29:32925716438853345300en388380690NaNI so cannot be bothered with the rest of the d...
27[πŸ˜‚][][@xxxtentacion]2017-11-01 13:29:35925716451457163300en899320696869974000NaN<USER> 2lit4life<EMOJI>
28[πŸ˜‚, πŸ™„][][]2017-11-01 13:29:35925716451461357600en784790670NaNI’m not stop saying that!<EMOJI><EMOJI>
29[πŸŽ‰, πŸŽ‚, 🎈, 🎊, 🎁, πŸ’œ][][@justinerooney_]2017-11-01 13:29:37925716459828936700en3051266655NaN<USER> HAPPY BIHDAY <EMOJI><EMOJI><EMOJI><EMOJ...
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68703[πŸ˜•][#halloweencostumes][]2017-11-01 07:23:04925624214522036200en1672876458NaN<HASHTAG> this one falls under the weird crazy...
68704[πŸ˜‚, 😩][][]2017-11-01 07:23:05925624218682777600en382473866NaNI'm not allowed to have chocolates yet, then I...
68705[πŸ˜‚][#MUFC][]2017-11-01 07:23:06925624222889766900en893145405457911800NaNManchester United manager Mourinho slams 'spec...
68706[πŸ’–][][]2017-11-01 07:23:07925624227088121900en240378516NaN<EMOJI> en Bushwhick
68707[πŸŒ†, πŸ‘‰, πŸš–, πŸ“ž][#BurkeCentre][]2017-11-01 07:23:18925624273237983200en784620573209002000NaN: <HASHTAG> <EMOJI> <EMOJI><EMOJI> For Taxi <E...
68708[😁][][@mychosliaheart, @BarrettoJulia, @iamjoshuaga...2017-11-01 07:23:18925624273212805100en1709981879.254136e+17<USER> <USER> <USER> Look, Mammeh and Daddeh! ...
68709[πŸ˜‹][][]2017-11-01 07:23:24925624298395533300en1348667816NaNLife is so good with you <EMOJI>
68710[πŸ‘Œ, πŸŽƒ, 😘][#portlandoregon, #portlandhalloween, #carrie…][]2017-11-01 07:23:24925624298378801200en722481645765300200NaNHappy Halloween! <EMOJI>🏽<EMOJI><EMOJI> <HASHT...
68711[πŸ‘][][@8limbsbondi...]2017-11-01 07:23:26925624306779897900en2443251500NaNSome work on the ropes in today’s boxing class...
68712[😭][][]2017-11-01 07:23:27925624310974136300en2406186390NaNScotty and Kristen’s halloween costumes <EMOJI>
68713[πŸ˜‚][][@rfrandrea, @AdaaanAndyyy]2017-11-01 07:23:30925624323557146600en10394481499.256150e+17<USER> <USER> May pre-month celebration sis <E...
68714[😭, 😭, πŸ’˜][][@peachshua1230]2017-11-01 07:23:31925624327755591700en8450855445896724009.256009e+17<USER> Awww <EMOJI> Ajsksjdjd im smiling like ...
68715[😫, βœ‹][][]2017-11-01 07:23:34925624340342812700en924752524871131100NaNI hate when I send a text or snap n I'm so anx...
68716[πŸ˜‚, πŸ™][][]2017-11-01 07:23:34925624340355280900en419493819NaNThe answer is no I have no plans and I never l...
68717[😭][][@BeachBoy_Gab]2017-11-01 07:23:34925624340346937300en24577459529.256219e+17<USER> LMAOOO I'm so proud <EMOJI>
68718[😒, πŸ’”][][]2017-11-01 07:23:34925624340338507800en1955767531NaNMy cousin/little sister is leaving to San Fran...
68719[πŸ™ƒ][][]2017-11-01 07:23:35925624344524361700en796490344581898200NaNCan't be alone w my thoughts tonight so just g...
68720[πŸ˜‚, πŸ˜‚, πŸ˜‚, πŸ˜‚, πŸ˜‚][][]2017-11-01 07:23:36925624348710285300en907808317124177900NaN<EMOJI><EMOJI><EMOJI><EMOJI><EMOJI> ambot!!!
68721[πŸ‘Œ, πŸ‘Š, πŸ™Œ][][]2017-11-01 07:23:37925624352929910800en262162415NaN<EMOJI>🏽<EMOJI>🏽 1st of the month!!Happy 1st o...
68722[😴][#WednesdayWisdom][]2017-11-01 07:23:41925624369715515400en574882525NaN<HASHTAG> ... stay in bed <EMOJI>
68723[πŸ˜‚, πŸ˜‚, πŸ˜‚, πŸ˜‚][][@Louis_Tomlinson, @NiallOfficial]2017-11-01 07:23:44925624382269124600en5561751739.254038e+17<USER> <USER> THIS IS GOLD. GOLD. <EMOJI><EMOJ...
68724[πŸ˜€][][]2017-11-01 07:23:45925624386455031800en1610265588NaN<EMOJI> thank you for the kind compliment
68725[😎][][]2017-11-01 07:23:45925624386454937600en4760724450NaNEnjoyed the silence <EMOJI>
68726[✨][][]2017-11-01 07:23:46925624390657572900en882858115636514800NaNOS: Spiderman Homecoming <EMOJI>
68727[🀷][][]2017-11-01 07:23:46925624390682849300en188129628NaNGo to hell <EMOJI>πŸ½β€β™€οΈ
68728[😘][][]2017-11-01 07:23:46925624390666129400en2473135939NaNThank you Yomi! <EMOJI>
68729[πŸ˜‚][][@discopiggu]2017-11-01 07:23:46925624390670106600en23735842099.256241e+17<USER> Lol. Just enjoy the stars. Music Kidhar...
68730[πŸ™][#NYCStrong][]2017-11-01 07:23:50925624407459971100en181689756NaNThoughts and prayers for NY<EMOJI>🏻 <HASHTAG>
68731[πŸ’][#GreatSuccess][@BrianyH]2017-11-01 07:23:50925624407460057100en601607889.254610e+17<USER> I searched COCK, PENIS, SHLONG, WINKY, ...
68732[πŸƒ, 🌻, 🌻, πŸƒ, πŸƒ, πŸ’, πŸ’, πŸƒ, πŸ™‹][][@amitbarman520]2017-11-01 07:23:53925624420022063100en37922907259.256215e+17<USER> Thank you so much<EMOJI><EMOJI><EMOJI><...
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68733 rows Γ— 9 columns

\n", "
" ], "text/plain": [ " EMOJI \\\n", "0 [πŸ”₯, πŸ‘] \n", "1 [🀦] \n", "2 [πŸ˜„] \n", "3 [πŸ“·] \n", "4 [😩, 😩] \n", "5 [😭, πŸ’“] \n", "6 [πŸ˜‚] \n", "7 [πŸ’―] \n", "8 [πŸ™„] \n", "9 [😟, πŸ˜₯, 😒] \n", "10 [🌻] \n", "11 [🍁, 🌺, πŸ‚] \n", "12 [πŸƒ] \n", "13 [πŸ’­, 🀦] \n", "14 [😍, 😘] \n", "15 [😜] \n", "16 [πŸ˜‚, πŸ”₯] \n", "17 [πŸ’•] \n", "18 [πŸ˜‰] \n", "19 [πŸ‘…] \n", "20 [πŸ‘Œ, πŸ™‚] \n", "21 [πŸ€—, πŸ“Ί] \n", "22 [πŸ’―] \n", "23 [πŸ‘…, πŸ’¦, πŸ‘] \n", "24 [πŸ’”] \n", "25 [🀐, 🀐, 🀐] \n", "26 [😴] \n", "27 [πŸ˜‚] \n", "28 [πŸ˜‚, πŸ™„] \n", "29 [πŸŽ‰, πŸŽ‚, 🎈, 🎊, 🎁, πŸ’œ] \n", "... ... \n", "68703 [πŸ˜•] \n", "68704 [πŸ˜‚, 😩] \n", "68705 [πŸ˜‚] \n", "68706 [πŸ’–] \n", "68707 [πŸŒ†, πŸ‘‰, πŸš–, πŸ“ž] \n", "68708 [😁] \n", "68709 [πŸ˜‹] \n", "68710 [πŸ‘Œ, πŸŽƒ, 😘] \n", "68711 [πŸ‘] \n", "68712 [😭] \n", "68713 [πŸ˜‚] \n", "68714 [😭, 😭, πŸ’˜] \n", "68715 [😫, βœ‹] \n", "68716 [πŸ˜‚, πŸ™] \n", "68717 [😭] \n", "68718 [😒, πŸ’”] \n", "68719 [πŸ™ƒ] \n", "68720 [πŸ˜‚, πŸ˜‚, πŸ˜‚, πŸ˜‚, πŸ˜‚] \n", "68721 [πŸ‘Œ, πŸ‘Š, πŸ™Œ] \n", "68722 [😴] \n", "68723 [πŸ˜‚, πŸ˜‚, πŸ˜‚, πŸ˜‚] \n", "68724 [πŸ˜€] \n", "68725 [😎] \n", "68726 [✨] \n", "68727 [🀷] \n", "68728 [😘] \n", "68729 [πŸ˜‚] \n", "68730 [πŸ™] \n", "68731 [πŸ’] \n", "68732 [πŸƒ, 🌻, 🌻, πŸƒ, πŸƒ, πŸ’, πŸ’, πŸƒ, πŸ™‹] \n", "\n", " HASHTAGS \\\n", "0 [] \n", "1 [] \n", "2 [] \n", "3 [] \n", "4 [] \n", "5 [] \n", "6 [] \n", "7 [] \n", "8 [] \n", "9 [] \n", "10 [] \n", "11 [] \n", "12 [#mortdale, #partofthefamily, #gorgeousboy] \n", "13 [] \n", "14 [#7YearsOfKMH2] \n", "15 [] \n", "16 [] \n", "17 [] \n", "18 [] \n", "19 [#footfetishnation] \n", "20 [] \n", "21 [] \n", "22 [] \n", "23 [] \n", "24 [] \n", "25 [] \n", "26 [] \n", "27 [] \n", "28 [] \n", "29 [] \n", "... ... \n", "68703 [#halloweencostumes] \n", "68704 [] \n", "68705 [#MUFC] \n", "68706 [] \n", "68707 [#BurkeCentre] \n", "68708 [] \n", "68709 [] \n", "68710 [#portlandoregon, #portlandhalloween, #carrie…] \n", "68711 [] \n", "68712 [] \n", "68713 [] \n", "68714 [] \n", "68715 [] \n", "68716 [] \n", "68717 [] \n", "68718 [] \n", "68719 [] \n", "68720 [] \n", "68721 [] \n", "68722 [#WednesdayWisdom] \n", "68723 [] \n", "68724 [] \n", "68725 [] \n", "68726 [] \n", "68727 [] \n", "68728 [] \n", "68729 [] \n", "68730 [#NYCStrong] \n", "68731 [#GreatSuccess] \n", "68732 [] \n", "\n", " LINKED_USER datetime \\\n", "0 [] 2017-11-01 13:29:00 \n", "1 [] 2017-11-01 13:29:00 \n", "2 [] 2017-11-01 13:29:03 \n", "3 [] 2017-11-01 13:29:04 \n", "4 [@hiphopphiIes] 2017-11-01 13:29:06 \n", "5 [@WizMommma] 2017-11-01 13:29:02 \n", "6 [] 2017-11-01 13:29:05 \n", "7 [] 2017-11-01 13:29:07 \n", "8 [@SeaDimon, @lsarsour] 2017-11-01 13:29:09 \n", "9 [@Ian_khetye] 2017-11-01 13:29:10 \n", "10 [] 2017-11-01 13:29:13 \n", "11 [@Dimafadma] 2017-11-01 13:29:15 \n", "12 [] 2017-11-01 13:29:16 \n", "13 [] 2017-11-01 13:29:20 \n", "14 [] 2017-11-01 13:29:20 \n", "15 [] 2017-11-01 13:29:22 \n", "16 [] 2017-11-01 13:29:23 \n", "17 [@yungbabytate] 2017-11-01 13:29:23 \n", "18 [@cmckenney] 2017-11-01 13:29:23 \n", "19 [] 2017-11-01 13:29:25 \n", "20 [] 2017-11-01 13:29:25 \n", "21 [] 2017-11-01 13:29:26 \n", "22 [] 2017-11-01 13:29:26 \n", "23 [@ctrlpurp] 2017-11-01 13:29:29 \n", "24 [@saunders_court1] 2017-11-01 13:29:30 \n", "25 [] 2017-11-01 13:29:31 \n", "26 [] 2017-11-01 13:29:32 \n", "27 [@xxxtentacion] 2017-11-01 13:29:35 \n", "28 [] 2017-11-01 13:29:35 \n", "29 [@justinerooney_] 2017-11-01 13:29:37 \n", "... ... ... \n", "68703 [] 2017-11-01 07:23:04 \n", "68704 [] 2017-11-01 07:23:05 \n", "68705 [] 2017-11-01 07:23:06 \n", "68706 [] 2017-11-01 07:23:07 \n", "68707 [] 2017-11-01 07:23:18 \n", "68708 [@mychosliaheart, @BarrettoJulia, @iamjoshuaga... 2017-11-01 07:23:18 \n", "68709 [] 2017-11-01 07:23:24 \n", "68710 [] 2017-11-01 07:23:24 \n", "68711 [@8limbsbondi...] 2017-11-01 07:23:26 \n", "68712 [] 2017-11-01 07:23:27 \n", "68713 [@rfrandrea, @AdaaanAndyyy] 2017-11-01 07:23:30 \n", "68714 [@peachshua1230] 2017-11-01 07:23:31 \n", "68715 [] 2017-11-01 07:23:34 \n", "68716 [] 2017-11-01 07:23:34 \n", "68717 [@BeachBoy_Gab] 2017-11-01 07:23:34 \n", "68718 [] 2017-11-01 07:23:34 \n", "68719 [] 2017-11-01 07:23:35 \n", "68720 [] 2017-11-01 07:23:36 \n", "68721 [] 2017-11-01 07:23:37 \n", "68722 [] 2017-11-01 07:23:41 \n", "68723 [@Louis_Tomlinson, @NiallOfficial] 2017-11-01 07:23:44 \n", "68724 [] 2017-11-01 07:23:45 \n", "68725 [] 2017-11-01 07:23:45 \n", "68726 [] 2017-11-01 07:23:46 \n", "68727 [] 2017-11-01 07:23:46 \n", "68728 [] 2017-11-01 07:23:46 \n", "68729 [@discopiggu] 2017-11-01 07:23:46 \n", "68730 [] 2017-11-01 07:23:50 \n", "68731 [@BrianyH] 2017-11-01 07:23:50 \n", "68732 [@amitbarman520] 2017-11-01 07:23:53 \n", "\n", " id lang person reply_to \\\n", "0 925716304635547600 en 31507978 NaN \n", "1 925716304664911900 en 231994649 NaN \n", "2 925716317214089200 en 2592765104 NaN \n", "3 925716321416949800 en 278737933 NaN \n", "4 925716329801310200 en 824586253634981900 9.257162e+17 \n", "5 925716313019965400 en 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925716430473039900 en 3471187337 9.257163e+17 \n", "25 925716434667184100 en 780060488600199200 NaN \n", "26 925716438853345300 en 388380690 NaN \n", "27 925716451457163300 en 899320696869974000 NaN \n", "28 925716451461357600 en 784790670 NaN \n", "29 925716459828936700 en 3051266655 NaN \n", "... ... ... ... ... \n", "68703 925624214522036200 en 1672876458 NaN \n", "68704 925624218682777600 en 382473866 NaN \n", "68705 925624222889766900 en 893145405457911800 NaN \n", "68706 925624227088121900 en 240378516 NaN \n", "68707 925624273237983200 en 784620573209002000 NaN \n", "68708 925624273212805100 en 170998187 9.254136e+17 \n", "68709 925624298395533300 en 1348667816 NaN \n", "68710 925624298378801200 en 722481645765300200 NaN \n", "68711 925624306779897900 en 2443251500 NaN \n", "68712 925624310974136300 en 2406186390 NaN \n", "68713 925624323557146600 en 1039448149 9.256150e+17 \n", "68714 925624327755591700 en 845085544589672400 9.256009e+17 \n", "68715 925624340342812700 en 924752524871131100 NaN \n", "68716 925624340355280900 en 419493819 NaN \n", "68717 925624340346937300 en 2457745952 9.256219e+17 \n", "68718 925624340338507800 en 1955767531 NaN \n", "68719 925624344524361700 en 796490344581898200 NaN \n", "68720 925624348710285300 en 907808317124177900 NaN \n", "68721 925624352929910800 en 262162415 NaN \n", "68722 925624369715515400 en 574882525 NaN \n", "68723 925624382269124600 en 556175173 9.254038e+17 \n", "68724 925624386455031800 en 1610265588 NaN \n", "68725 925624386454937600 en 4760724450 NaN \n", "68726 925624390657572900 en 882858115636514800 NaN \n", "68727 925624390682849300 en 188129628 NaN \n", "68728 925624390666129400 en 2473135939 NaN \n", "68729 925624390670106600 en 2373584209 9.256241e+17 \n", "68730 925624407459971100 en 181689756 NaN \n", "68731 925624407460057100 en 60160788 9.254610e+17 \n", "68732 925624420022063100 en 3792290725 9.256215e+17 \n", "\n", " text \n", "0 fashionbombdaily's photo 🏼 \n", "1 It’s scary how on point my horoscope be \n", "3 vivalcli: Portraits by Zhao Guojing an... \n", "4 i wanna know too \n", "5 veda was yoda too \n", "6 I’m less stressed about turning 30 now ... \n", "7 Full charged. \n", "8 That’s part of the problem, (they) got me emotional there \n", "11 Happy month to you and your loved ones ... \n", "12 Maxx and Patricia. Family hangs at For Good He... \n", "13 I need to STOP beating myself up with my thoug... \n", "14 Cutest Son Roll no. 31 \n", "15 By the summer I should have everything up and ... \n", "16 I know my English is not that good but that do... \n", "17 I u mama \n", "18 That picture was NOT taken this morning... \n", "19 Welcome to \n", "20 Awkward \n", "21 back at it with supernatural \n", "22 One of the best things I've learned was to sto... \n", "23 Can I taste? \n", "24 we miss you ☹️ \n", "25 Actually my bias in WJSN are Eunseo & Bona... \n", "26 I so cannot be bothered with the rest of the d... \n", "27 2lit4life \n", "28 I’m not stop saying that! \n", "29 HAPPY BIHDAY this one falls under the weird crazy... \n", "68704 I'm not allowed to have chocolates yet, then I... \n", "68705 Manchester United manager Mourinho slams 'spec... \n", "68706 en Bushwhick \n", "68707 : For Taxi Look, Mammeh and Daddeh! ... \n", "68709 Life is so good with you \n", "68710 Happy Halloween! 🏽 \n", "68713 May pre-month celebration sis Awww Ajsksjdjd im smiling like ... \n", "68715 I hate when I send a text or snap n I'm so anx... \n", "68716 The answer is no I have no plans and I never l... \n", "68717 LMAOOO I'm so proud \n", "68718 My cousin/little sister is leaving to San Fran... \n", "68719 Can't be alone w my thoughts tonight so just g... \n", "68720 ambot!!! \n", "68721 🏽🏽 1st of the month!!Happy 1st o... \n", "68722 ... stay in bed \n", "68723 THIS IS GOLD. GOLD. thank you for the kind compliment \n", "68725 Enjoyed the silence \n", "68726 OS: Spiderman Homecoming \n", "68727 Go to hell πŸ½β€β™€οΈ \n", "68728 Thank you Yomi! \n", "68729 Lol. Just enjoy the stars. Music Kidhar... \n", "68730 Thoughts and prayers for NY🏻 \n", "68731 I searched COCK, PENIS, SHLONG, WINKY, ... \n", "68732 Thank you so much<... \n", "\n", "[68733 rows x 9 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "twitter_data = pd.read_json(json_files[0], encoding=\"utf-8\")\n", "twitter_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* extracting emojis and text" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "emojis = twitter_data['EMOJI']\n", "plain_text = twitter_data['text']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* make our plain text more \"plain\":\n", " * removing the keyword `` (just for the beginning)\n", " * removing remaining useless emojis, like skin modifier etc." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# defining blacklist for modifier emojis:\n", "emoji_blacklist = set([\n", " chr(0x1F3FB),\n", " chr(0x1F3FC),\n", " chr(0x1F3FD),\n", " chr(0x1F3FE),\n", " chr(0x1F3FF),\n", " chr(0x2642),\n", " chr(0x2640)\n", "])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# filtering them and the EMOJI keyword out:\n", "plain_text = plain_text.str.replace(\"(||)\",\"\").str.replace(\"[\" + \"\".join(list(emoji_blacklist)) + \"]\",\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* defining different criterias for choosing a single emoji (currently `latest` is used)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def latest(lst):\n", " return lst[-1] if len(lst) > 0 else 'X' \n", "def most_common(lst):\n", " # trying to find the most common used emoji in the given lst\n", " return max(set(lst), key=lst.count) if len(lst) > 0 else \"X\" # setting label to 'X' if there is an empty emoji list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* convert all emojis to a sentiment vector" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "labels = emoji2sent([latest(e) for e in emojis])\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "68733" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(labels)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "wrong_labels = np.isnan(np.linalg.norm(labels, axis=1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* remove all data we have no label for" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "labels = labels[np.invert(wrong_labels)]\n", "plain_text = plain_text[np.invert(wrong_labels)]\n", "emojis = emojis[np.invert(wrong_labels)]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "33368 33368 33368\n" ] } ], "source": [ "print(len(labels), len(emojis), len(plain_text))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Apply stemming and lemmatization (if needed)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "from nltk.stem.snowball import SnowballStemmer\n", "from nltk.stem import WordNetLemmatizer\n", "from nltk import pos_tag\n", "from nltk import word_tokenize\n", "from nltk.corpus import wordnet" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def get_wordnet_pos(treebank_tag):\n", "\n", " if treebank_tag.startswith('J'):\n", " return wordnet.ADJ\n", " elif treebank_tag.startswith('V'):\n", " return wordnet.VERB\n", " elif treebank_tag.startswith('N'):\n", " return wordnet.NOUN\n", " elif treebank_tag.startswith('R'):\n", " return wordnet.ADV\n", " else:\n", " return wordnet.NOUN" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "stemmer = SnowballStemmer(\"english\")\n", "for key in plain_text.keys():\n", " stemmed_sent = []\n", " for word in plain_text[key].split(\" \"):\n", " word_stemmed = stemmer.stem(word)\n", " stemmed_sent.append(word_stemmed)\n", " stemmed_sent = (\" \").join(stemmed_sent)\n", " plain_text[key] = stemmed_sent" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 woooaaaahhh\n", "4 i wan na know too\n", "6 i 'm le stress about turn 30 now i think i'v r...\n", "9 got me emot there\n", "14 cutest son roll no . 31\n", "15 by the summer i should have everyth up and run...\n", "18 that pictur wa not taken this morning !\n", "26 i so can not be bother with the rest of the da...\n", "27 2lit4lif\n", "35 hate fall asleep befor i put my phone on the c...\n", "36 unexpect saw two of my crush today . this day ...\n", "40 elvi whi o whi ? our girl wa such a love stori...\n", "42 you'r late i ate them all\n", "43 me toooo\n", "47 the pressur is just too much\n", "51 i broke grammar\n", "52 have not desir to go to work today\n", "53 omg do n't it scari all i know is that i do no...\n", "56 achoo mr. fuck nigga you , you done caught cau...\n", "58 i can never catch a dang break !\n", "59 pas my p on two hour of sleep\n", "60 i 'm realli not amus\n", "65 i can help you\n", "71 whew i slept good af last night\n", "74 this would be epic . pizza and play perfect gi...\n", "76 hey , it 1st novemb\n", "80 u is to press bitch for me to have been speak ...\n", "88 lmfao thought it wa just me be bitter\n", "89 yupp yuppp . super prettttyyy , my heart cant ...\n", "90 bakit halo halong seri binanggit mo be ? none ...\n", " ... \n", "68675 go back to dark hair tomorrow , mhmm yasss\n", "68677 i miss them so much\n", "68678 i wan na feel your gut too\n", "68683 everi time\n", "68687 i neither own nor watch tv . now go watch cnn\n", "68688 revolutionari love\n", "68694 ear worm is run in the famili after sing an aw...\n", "68696 ill never look at you the same . yeah you got ...\n", "68699 it our 3 year anniversari today to celebrate ,...\n", "68700 person that scare me\n", "68701 damn girl . can u look ani hotter than this ? ...\n", "68703 this one fall under the weird crazi one .\n", "68704 i 'm not allow to have chocol yet , then i uni...\n", "68705 manchest unit manag mourinho slam specialists'...\n", "68708 look , mammeh and daddeh ! cuuutee..\n", "68709 life is so good with you\n", "68710 happi halloween !\n", "68712 scotti and kristen halloween costum\n", "68713 may pre-month celebr si\n", "68717 lmaooo i 'm so proud\n", "68720 ambot ! ! !\n", "68721 1st of the month ! ! happi 1st of novemb *53 d...\n", "68722 ... stay in bed\n", "68723 this is gold . gold .\n", "68724 thank you for the kind compliment\n", "68725 enjoy the silenc\n", "68728 thank you yomi !\n", "68729 lol . just enjoy the star . music kidhar aur b...\n", "68730 thought and prayer for ny\n", "68732 thank you so muchhav a happi wednesday and a g...\n", "Name: text, Length: 33368, dtype: object\n" ] } ], "source": [ "lemmatizer = WordNetLemmatizer()\n", "for key in plain_text.keys():\n", " lemmatized_sent = []\n", " sent_pos = pos_tag(word_tokenize(plain_text[key]))\n", " for word in sent_pos:\n", " wordnet_pos = get_wordnet_pos(word[1].lower())\n", " word_lemmatized = lemmatizer.lemmatize(word[0], pos=wordnet_pos)\n", " lemmatized_sent.append(word_lemmatized)\n", " lemmatized_sent = (\" \").join(lemmatized_sent)\n", " plain_text[key] = lemmatized_sent\n", "print(plain_text)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* generate weights:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# at first count over our table\n", "emoji_count = {}\n", "\n", "\n", "for e_list in emojis:\n", " for e in set(e_list):\n", " if e not in emoji_count:\n", " emoji_count[e] = 0\n", " emoji_count[e] += 1\n", "\n", "emoji_count\n", "emoji_sum = sum([emoji_count[e] for e in emoji_count])\n", "\n", "emoji_weights = {}\n", "for e in emoji_count:\n", " # tfidf for emojis\n", " emoji_weights[e] = np.log((emoji_sum / emoji_count[e]))\n", "\n", "weights_sum= sum([emoji_weights[x] for x in emoji_weights])\n", " \n", "# normalize:\n", "for e in emoji_weights:\n", " emoji_weights[e] = emoji_weights[e] / weights_sum\n", "\n", "emoji_weights['X'] = 0 # dummy values\n", "emoji_count['X'] = 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* most used emojis in Dataset" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "import operator" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('πŸ˜‚', 10182),\n", " ('😭', 3893),\n", " ('😍', 2866),\n", " ('😩', 1647),\n", " ('😊', 1450),\n", " ('😘', 1151),\n", " ('πŸ™', 1089),\n", " ('πŸ™Œ', 1003),\n", " ('πŸ˜‰', 752),\n", " ('😁', 697),\n", " ('πŸ˜…', 651),\n", " ('😎', 606),\n", " ('😒', 544),\n", " ('πŸ˜’', 539),\n", " ('😏', 478),\n", " ('😌', 434),\n", " ('πŸ˜”', 415),\n", " ('πŸ˜‹', 397),\n", " ('πŸ˜€', 392),\n", " ('😀', 368)]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "['πŸ˜‚',\n", " '😭',\n", " '😍',\n", " '😩',\n", " '😊',\n", " '😘',\n", " 'πŸ™',\n", " 'πŸ™Œ',\n", " 'πŸ˜‰',\n", " '😁',\n", " 'πŸ˜…',\n", " '😎',\n", " '😒',\n", " 'πŸ˜’',\n", " '😏',\n", " '😌',\n", " 'πŸ˜”',\n", " 'πŸ˜‹',\n", " 'πŸ˜€',\n", " '😀']" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sorted_emoji_count = list(reversed(sorted(emoji_count.items(), key=operator.itemgetter(1))))\n", "display(sorted_emoji_count[:20])\n", "\n", "top_emojis = [x[0] for x in sorted_emoji_count[:20]]\n", "display(top_emojis)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* only learn the most used ones:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "26197 26197 26197\n" ] } ], "source": [ "in_top = [sentiment_vector_to_emoji(x) in top_emojis for x in labels]\n", "labels = labels[in_top]\n", "plain_text = plain_text[in_top]\n", "emojis = emojis[in_top]\n", "print(len(labels), len(emojis), len(plain_text))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* generating train and test set:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "X1, Xt1, y1, yt1 = train_test_split(plain_text, labels, test_size=0.1, random_state=4222)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "#y1_weights = np.array([(sum([emoji_weights[e] for e in e_list]) / len(e_list)) if len(e_list) > 0 else 0 for e_list in sent2emoji(y1)])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "vectorizer = TfidfVectorizer(stop_words='english')\n", "vec_train = vectorizer.fit_transform(X1)\n", "vec_test = vectorizer.transform(Xt1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* train. this can take a very long time..." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "from sklearn.neural_network import MLPClassifier as MLP\n", "from sklearn.multiclass import OneVsRestClassifier as OVRC\n", "from sklearn.tree import DecisionTreeClassifier as DTC\n", "\n", "from keras.models import Sequential\n", "from keras.layers import Dense" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "def train(max_size = 10000, layers=[(1024, 'relu'),(y1[0].shape[0],'softmax')], random_state=4222, ovrc=False, n_iter=5):\n", " \n", " model = Sequential()\n", " \n", " # build mlp layers:\n", " keras_layers = []\n", " first_layer = True\n", " for layer in layers:\n", " if first_layer:\n", " model.add(Dense(units=layer[0], activation=layer[1], input_dim=vectorizer.transform([\" \"])[0]._shape[1]))\n", " first_layer = False\n", " else:\n", " model.add(Dense(units=layer[0], activation=layer[1]))\n", " \n", " #mlp = MLPClassifier(layers=sknn_layers, random_state=random_state, verbose=True, n_iter=n_iter, batch_size=100)\n", " \n", " model.compile(loss='mean_squared_error',\n", " optimizer='adam')\n", " \n", " clf = OVRC(model) if ovrc else model\n", "\n", " clf.fit(vec_train[:max_size].A, y1[:max_size], validation_split=0.2, epochs=n_iter)#, sample_weight=y1_weights[:max_size])\n", " \n", " return clf" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 18861 samples, validate on 4716 samples\n", "Epoch 1/3\n", "18861/18861 [==============================] - 1106s 59ms/step - loss: 0.0185 - val_loss: 0.0152\n", "Epoch 2/3\n", "18861/18861 [==============================] - 1104s 59ms/step - loss: 0.0107 - val_loss: 0.0163\n", "Epoch 3/3\n", "18861/18861 [==============================] - 1106s 59ms/step - loss: 0.0065 - val_loss: 0.0166\n" ] } ], "source": [ "clf = train(max_size=100000,layers=[(10000, 'relu'),(5000, 'relu'),(2500, 'relu'),(y1[0].shape[0],None)], n_iter=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* make a prediction and store it in a csv file:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "pred = clf.predict(vectorizer.transform(Xt1))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.011668838\n", "0.03387511671001757\n" ] } ], "source": [ "print(np.linalg.norm(np.var(pred, axis=0)))\n", "print(np.linalg.norm(np.var(labels, axis=0)))\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "# build a dataframe to visualize test results:\n", "testlist = pd.DataFrame({'text': Xt1, \n", " 'teacher': sent2emoji(yt1),\n", " 'teacher_sentiment': yt1.tolist(),\n", " 'predict': sent2emoji(pred, custom_target_emojis=top_emojis),\n", " 'predicted_sentiment': pred.tolist()})" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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textteacherteacher_sentimentpredictpredicted_sentiment
35671i feel like i care so much more in everi situatπŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...πŸ˜…[0.4447824954986572, 0.30056363344192505, 0.27...
25683i did not meat to add that 2 there ... hav see...πŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...😁[0.5660845637321472, 0.1737498641014099, 0.284...
8985neverβ€¦πŸ˜Š[0.7040175768989329, 0.059322033898305086, 0.2...πŸ˜‚[0.4871470034122467, 0.26607102155685425, 0.27...
5410lmao on me ! ! ! wtf wa he suppos to sayπŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...😒[0.4061833620071411, 0.3226468861103058, 0.273...
62611this dude alway help me get through my school ...😊[0.7040175768989329, 0.059322033898305086, 0.2...πŸ˜‚[0.4549962878227234, 0.21886931359767914, 0.33...
48197happi b'day sir😊[0.7040175768989329, 0.059322033898305086, 0.2...πŸ˜€[0.6561306715011597, 0.11821962147951126, 0.27...
23654you need some good old fashion swedish jesusπŸ™[0.4983755685510071, 0.08057179987004548, 0.42...πŸ˜…[0.4600130021572113, 0.28595462441444397, 0.27...
58207these late shift are make me not have a social...πŸ˜…[0.47186147186147187, 0.2922077922077922, 0.23...πŸ˜‚[0.49543458223342896, 0.25571855902671814, 0.2...
374dc this weekend😍[0.7296744771190439, 0.05173769460607014, 0.21...😌[0.6040589213371277, 0.15823380649089813, 0.26...
26310paul lad you 'll make e blush😊[0.7040175768989329, 0.059322033898305086, 0.2...πŸ˜‚[0.452500581741333, 0.2882971167564392, 0.2790...
30892did you have a fun halloween ?πŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...πŸ˜‚[0.4644194543361664, 0.2708289325237274, 0.277...
11868hi handsom😍[0.7296744771190439, 0.05173769460607014, 0.21...😊[0.7254493236541748, 0.12355809658765793, 0.22...
46219i 'm not okay with this , i 'm su snapchat😭[0.34310532030401736, 0.4364820846905538, 0.22...πŸ˜”[0.3264158070087433, 0.48023173213005066, 0.23...
13583my parent be so mad i be buy new stuff & amp ;...😭[0.34310532030401736, 0.4364820846905538, 0.22...πŸ˜‚[0.4271591007709503, 0.29361462593078613, 0.29...
43843one of the few song that calm me down esp on f...πŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...πŸ˜‚[0.44168680906295776, 0.2790682315826416, 0.29...
63589iphon x bouta be the last phone we ever buyπŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...πŸ˜…[0.41863512992858887, 0.3106093108654022, 0.28...
53695visit my main man today ❀ i miss u papa😭[0.34310532030401736, 0.4364820846905538, 0.22...😁[0.5650997757911682, 0.19236208498477936, 0.27...
67529donut😍[0.7296744771190439, 0.05173769460607014, 0.21...πŸ˜‚[0.45511549711227417, 0.28582143783569336, 0.2...
25493ha anyon heard this by ? who the fuck knew he ...😘[0.7546600877192983, 0.05290570175438596, 0.19...πŸ˜…[0.4276219606399536, 0.30413898825645447, 0.28...
19486wow superrbb😍[0.7296744771190439, 0.05173769460607014, 0.21...😊[0.7149834036827087, 0.10459273308515549, 0.24...
48449of cours they do n't . their perfect model of ...πŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...πŸ˜‚[0.5363025665283203, 0.22163532674312592, 0.27...
4504plea pick me . pick me . pick me . please .😍[0.7296744771190439, 0.05173769460607014, 0.21...πŸ˜‚[0.4641677737236023, 0.18824045360088348, 0.37...
40285shiid no crack is wack😭[0.34310532030401736, 0.4364820846905538, 0.22...πŸ˜‚[0.44292521476745605, 0.28201037645339966, 0.2...
56741pj still sleep like a newborn😩[0.22289823008849557, 0.5912610619469026, 0.18...😒[0.40168094635009766, 0.3777309060096741, 0.24...
22948an opinion doe n't mean you make sen first off...😌[0.6240601503759399, 0.13984962406015036, 0.23...πŸ˜‚[0.4365620017051697, 0.2830066680908203, 0.294...
68426missyou too😘[0.7546600877192983, 0.05290570175438596, 0.19...😭[0.3452186584472656, 0.4593580365180969, 0.223...
13431i swear she did😩[0.22289823008849557, 0.5912610619469026, 0.18...πŸ˜‚[0.4436468482017517, 0.2736954987049103, 0.294...
66287it true , he wa the mutt ( big-d ) nut .πŸ˜‰[0.5634451019066403, 0.0992767915844839, 0.337...πŸ˜…[0.47334975004196167, 0.2881445586681366, 0.26...
41980is happen so happi😭[0.34310532030401736, 0.4364820846905538, 0.22...πŸ˜…[0.4635038673877716, 0.30251604318618774, 0.26...
34632saw that the first one said sose you tmmrw and npπŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...πŸ˜…[0.43297499418258667, 0.31000325083732605, 0.2...
..................
47218keep it i do n't want itπŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...πŸ˜…[0.470976859331131, 0.2997904419898987, 0.2489...
350876 year ago today we said our final good bye , ...😒[0.39118825100133514, 0.38451268357810414, 0.2...πŸ˜…[0.4478594660758972, 0.30181050300598145, 0.27...
61252amenπŸ™[0.4983755685510071, 0.08057179987004548, 0.42...😁[0.5783949494361877, 0.15405726432800293, 0.30...
39960fact😎[0.5981432360742706, 0.10477453580901856, 0.29...πŸ˜‚[0.4938848316669464, 0.24237176775932312, 0.25...
365nw final found someon hi loydiπŸ˜…[0.47186147186147187, 0.2922077922077922, 0.23...😌[0.6203110218048096, 0.17863908410072327, 0.23...
50665i need ur shoulder to cri on😒[0.39118825100133514, 0.38451268357810414, 0.2...πŸ˜…[0.4619033932685852, 0.2977892756462097, 0.267...
21007awww you 'll get me teari eye gurl !😘[0.7546600877192983, 0.05290570175438596, 0.19...πŸ˜…[0.5029815435409546, 0.2711288630962372, 0.253...
18819γ€°happi hump day to all my ladi is def sweet like😘[0.7546600877192983, 0.05290570175438596, 0.19...πŸ™Œ[0.6974080801010132, 0.11416944861412048, 0.24...
42421thanks .😊[0.7040175768989329, 0.059322033898305086, 0.2...😁[0.5765037536621094, 0.18341206014156342, 0.26...
38705it okay i did n't see you until i wa in your faceπŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...😒[0.4030645787715912, 0.36543628573417664, 0.25...
47033fall in with alexissdang β¬…οΈπŸ˜[0.7296744771190439, 0.05173769460607014, 0.21...😌[0.6219494342803955, 0.1795988380908966, 0.226...
38742let your wild side free girl ! ! ! ! !πŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...😒[0.4049544930458069, 0.35102248191833496, 0.25...
67610can we get season 3 already ! ?😭[0.34310532030401736, 0.4364820846905538, 0.22...😒[0.416861355304718, 0.35468244552612305, 0.245...
61021have to quot this bih rememb this niggaπŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...πŸ˜‚[0.4401417672634125, 0.2810788154602051, 0.292...
14904he so damn😩[0.22289823008849557, 0.5912610619469026, 0.18...πŸ˜‚[0.5411785244941711, 0.2148503214120865, 0.246...
50994yasss it time for a great show maxloyalβ„’β™› : gi...πŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...πŸ™Œ[0.7073097229003906, 0.12480126321315765, 0.23...
6973got to love a fish finger sarniπŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...😌[0.6375303864479065, 0.14495858550071716, 0.24...
3269say datπŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...πŸ˜‚[0.5052645802497864, 0.21280284225940704, 0.30...
54827if ur happi i 'm happi😊[0.7040175768989329, 0.059322033898305086, 0.2...πŸ˜…[0.5138391852378845, 0.26520460844039917, 0.25...
19409rockstarπŸ˜€[0.6560364464692483, 0.08428246013667426, 0.25...πŸ˜‚[0.520979106426239, 0.22392335534095764, 0.265...
38703god fuck me i alreadi fix it oncπŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...πŸ˜”[0.32009249925613403, 0.4819949269294739, 0.22...
25133it social accept to listen to ani christma mus...πŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...😁[0.5703040361404419, 0.17875489592552185, 0.27...
15339i thrash p in imessag now he wan na bet on 2kπŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...πŸ˜…[0.4571400284767151, 0.2949279546737671, 0.269...
28082my fatass need some lemon pepper wing w a lil ...πŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...😭[0.36291953921318054, 0.4310339391231537, 0.22...
44462fck off tristan yes soph collect u in 15 n we ...😀[0.2691131498470948, 0.4801223241590214, 0.250...😌[0.6122021675109863, 0.15579015016555786, 0.27...
60212open the bagπŸ˜‹[0.6784741144414169, 0.04495912806539509, 0.27...πŸ˜‚[0.470508873462677, 0.2711063623428345, 0.2810...
34950i ask peopl to guess my zodiac sign and this o...😀[0.2691131498470948, 0.4801223241590214, 0.250...πŸ˜‚[0.4431285262107849, 0.27477312088012695, 0.29...
59462i regret this smπŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...😒[0.40375131368637085, 0.3289208710193634, 0.27...
19465fuck collegπŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...πŸ˜…[0.4494284391403198, 0.31076472997665405, 0.26...
8135aye this wa my first time actual see it wendi ...πŸ˜‚[0.46813021474490496, 0.24716181096977158, 0.2...πŸ˜…[0.42708122730255127, 0.31392431259155273, 0.2...
\n", "

2620 rows Γ— 5 columns

\n", "
" ], "text/plain": [ " text teacher \\\n", "35671 i feel like i care so much more in everi situat πŸ˜‚ \n", "25683 i did not meat to add that 2 there ... hav see... πŸ˜‚ \n", "8985 never… 😊 \n", "5410 lmao on me ! ! ! wtf wa he suppos to say πŸ˜‚ \n", "62611 this dude alway help me get through my school ... 😊 \n", "48197 happi b'day sir 😊 \n", "23654 you need some good old fashion swedish jesus πŸ™ \n", "58207 these late shift are make me not have a social... πŸ˜… \n", "374 dc this weekend 😍 \n", "26310 paul lad you 'll make e blush 😊 \n", "30892 did you have a fun halloween ? πŸ˜‚ \n", "11868 hi handsom 😍 \n", "46219 i 'm not okay with this , i 'm su snapchat 😭 \n", "13583 my parent be so mad i be buy new stuff & amp ;... 😭 \n", "43843 one of the few song that calm me down esp on f... πŸ˜‚ \n", "63589 iphon x bouta be the last phone we ever buy πŸ˜‚ \n", "53695 visit my main man today ❀ i miss u papa 😭 \n", "67529 donut 😍 \n", "25493 ha anyon heard this by ? who the fuck knew he ... 😘 \n", "19486 wow superrbb 😍 \n", "48449 of cours they do n't . their perfect model of ... πŸ˜‚ \n", "4504 plea pick me . pick me . pick me . please . 😍 \n", "40285 shiid no crack is wack 😭 \n", "56741 pj still sleep like a newborn 😩 \n", "22948 an opinion doe n't mean you make sen first off... 😌 \n", "68426 missyou too 😘 \n", "13431 i swear she did 😩 \n", "66287 it true , he wa the mutt ( big-d ) nut . πŸ˜‰ \n", "41980 is happen so happi 😭 \n", "34632 saw that the first one said sose you tmmrw and np πŸ˜‚ \n", "... ... ... \n", "47218 keep it i do n't want it πŸ˜‚ \n", "35087 6 year ago today we said our final good bye , ... 😒 \n", "61252 amen πŸ™ \n", "39960 fact 😎 \n", "365 nw final found someon hi loydi πŸ˜… \n", "50665 i need ur shoulder to cri on 😒 \n", "21007 awww you 'll get me teari eye gurl ! 😘 \n", "18819 γ€°happi hump day to all my ladi is def sweet like 😘 \n", "42421 thanks . 😊 \n", "38705 it okay i did n't see you until i wa in your face πŸ˜‚ \n", "47033 fall in with alexissdang ⬅️ 😍 \n", "38742 let your wild side free girl ! ! ! ! ! πŸ˜‚ \n", "67610 can we get season 3 already ! ? 😭 \n", "61021 have to quot this bih rememb this nigga πŸ˜‚ \n", "14904 he so damn 😩 \n", "50994 yasss it time for a great show maxloyalβ„’β™› : gi... πŸ˜‚ \n", "6973 got to love a fish finger sarni πŸ˜‚ \n", "3269 say dat πŸ˜‚ \n", "54827 if ur happi i 'm happi 😊 \n", "19409 rockstar πŸ˜€ \n", "38703 god fuck me i alreadi fix it onc πŸ˜‚ \n", "25133 it social accept to listen to ani christma mus... πŸ˜‚ \n", "15339 i thrash p in imessag now he wan na bet on 2k πŸ˜‚ \n", "28082 my fatass need some lemon pepper wing w a lil ... πŸ˜‚ \n", "44462 fck off tristan yes soph collect u in 15 n we ... 😀 \n", "60212 open the bag πŸ˜‹ \n", "34950 i ask peopl to guess my zodiac sign and this o... 😀 \n", "59462 i regret this sm πŸ˜‚ \n", "19465 fuck colleg πŸ˜‚ \n", "8135 aye this wa my first time actual see it wendi ... πŸ˜‚ \n", "\n", " teacher_sentiment predict \\\n", "35671 [0.46813021474490496, 0.24716181096977158, 0.2... πŸ˜… \n", "25683 [0.46813021474490496, 0.24716181096977158, 0.2... 😁 \n", "8985 [0.7040175768989329, 0.059322033898305086, 0.2... πŸ˜‚ \n", "5410 [0.46813021474490496, 0.24716181096977158, 0.2... 😒 \n", "62611 [0.7040175768989329, 0.059322033898305086, 0.2... πŸ˜‚ \n", "48197 [0.7040175768989329, 0.059322033898305086, 0.2... πŸ˜€ \n", "23654 [0.4983755685510071, 0.08057179987004548, 0.42... πŸ˜… \n", "58207 [0.47186147186147187, 0.2922077922077922, 0.23... πŸ˜‚ \n", "374 [0.7296744771190439, 0.05173769460607014, 0.21... 😌 \n", "26310 [0.7040175768989329, 0.059322033898305086, 0.2... πŸ˜‚ \n", "30892 [0.46813021474490496, 0.24716181096977158, 0.2... πŸ˜‚ \n", "11868 [0.7296744771190439, 0.05173769460607014, 0.21... 😊 \n", "46219 [0.34310532030401736, 0.4364820846905538, 0.22... πŸ˜” \n", "13583 [0.34310532030401736, 0.4364820846905538, 0.22... πŸ˜‚ \n", "43843 [0.46813021474490496, 0.24716181096977158, 0.2... πŸ˜‚ \n", "63589 [0.46813021474490496, 0.24716181096977158, 0.2... πŸ˜… \n", "53695 [0.34310532030401736, 0.4364820846905538, 0.22... 😁 \n", "67529 [0.7296744771190439, 0.05173769460607014, 0.21... πŸ˜‚ \n", "25493 [0.7546600877192983, 0.05290570175438596, 0.19... πŸ˜… \n", "19486 [0.7296744771190439, 0.05173769460607014, 0.21... 😊 \n", "48449 [0.46813021474490496, 0.24716181096977158, 0.2... πŸ˜‚ \n", "4504 [0.7296744771190439, 0.05173769460607014, 0.21... πŸ˜‚ \n", "40285 [0.34310532030401736, 0.4364820846905538, 0.22... πŸ˜‚ \n", "56741 [0.22289823008849557, 0.5912610619469026, 0.18... 😒 \n", "22948 [0.6240601503759399, 0.13984962406015036, 0.23... πŸ˜‚ \n", "68426 [0.7546600877192983, 0.05290570175438596, 0.19... 😭 \n", "13431 [0.22289823008849557, 0.5912610619469026, 0.18... πŸ˜‚ \n", "66287 [0.5634451019066403, 0.0992767915844839, 0.337... πŸ˜… \n", "41980 [0.34310532030401736, 0.4364820846905538, 0.22... πŸ˜… \n", "34632 [0.46813021474490496, 0.24716181096977158, 0.2... πŸ˜… \n", "... ... ... \n", "47218 [0.46813021474490496, 0.24716181096977158, 0.2... πŸ˜… \n", "35087 [0.39118825100133514, 0.38451268357810414, 0.2... πŸ˜… \n", "61252 [0.4983755685510071, 0.08057179987004548, 0.42... 😁 \n", "39960 [0.5981432360742706, 0.10477453580901856, 0.29... πŸ˜‚ \n", "365 [0.47186147186147187, 0.2922077922077922, 0.23... 😌 \n", "50665 [0.39118825100133514, 0.38451268357810414, 0.2... πŸ˜… \n", "21007 [0.7546600877192983, 0.05290570175438596, 0.19... πŸ˜… \n", "18819 [0.7546600877192983, 0.05290570175438596, 0.19... πŸ™Œ \n", "42421 [0.7040175768989329, 0.059322033898305086, 0.2... 😁 \n", "38705 [0.46813021474490496, 0.24716181096977158, 0.2... 😒 \n", "47033 [0.7296744771190439, 0.05173769460607014, 0.21... 😌 \n", "38742 [0.46813021474490496, 0.24716181096977158, 0.2... 😒 \n", "67610 [0.34310532030401736, 0.4364820846905538, 0.22... 😒 \n", "61021 [0.46813021474490496, 0.24716181096977158, 0.2... πŸ˜‚ \n", "14904 [0.22289823008849557, 0.5912610619469026, 0.18... πŸ˜‚ \n", "50994 [0.46813021474490496, 0.24716181096977158, 0.2... πŸ™Œ \n", "6973 [0.46813021474490496, 0.24716181096977158, 0.2... 😌 \n", "3269 [0.46813021474490496, 0.24716181096977158, 0.2... πŸ˜‚ \n", "54827 [0.7040175768989329, 0.059322033898305086, 0.2... πŸ˜… \n", "19409 [0.6560364464692483, 0.08428246013667426, 0.25... πŸ˜‚ \n", "38703 [0.46813021474490496, 0.24716181096977158, 0.2... πŸ˜” \n", "25133 [0.46813021474490496, 0.24716181096977158, 0.2... 😁 \n", "15339 [0.46813021474490496, 0.24716181096977158, 0.2... πŸ˜… \n", "28082 [0.46813021474490496, 0.24716181096977158, 0.2... 😭 \n", "44462 [0.2691131498470948, 0.4801223241590214, 0.250... 😌 \n", "60212 [0.6784741144414169, 0.04495912806539509, 0.27... πŸ˜‚ \n", "34950 [0.2691131498470948, 0.4801223241590214, 0.250... πŸ˜‚ \n", "59462 [0.46813021474490496, 0.24716181096977158, 0.2... 😒 \n", "19465 [0.46813021474490496, 0.24716181096977158, 0.2... πŸ˜… \n", "8135 [0.46813021474490496, 0.24716181096977158, 0.2... πŸ˜… \n", "\n", " predicted_sentiment \n", "35671 [0.4447824954986572, 0.30056363344192505, 0.27... \n", "25683 [0.5660845637321472, 0.1737498641014099, 0.284... \n", "8985 [0.4871470034122467, 0.26607102155685425, 0.27... \n", "5410 [0.4061833620071411, 0.3226468861103058, 0.273... \n", "62611 [0.4549962878227234, 0.21886931359767914, 0.33... \n", "48197 [0.6561306715011597, 0.11821962147951126, 0.27... \n", "23654 [0.4600130021572113, 0.28595462441444397, 0.27... \n", "58207 [0.49543458223342896, 0.25571855902671814, 0.2... \n", "374 [0.6040589213371277, 0.15823380649089813, 0.26... \n", "26310 [0.452500581741333, 0.2882971167564392, 0.2790... \n", "30892 [0.4644194543361664, 0.2708289325237274, 0.277... \n", "11868 [0.7254493236541748, 0.12355809658765793, 0.22... \n", "46219 [0.3264158070087433, 0.48023173213005066, 0.23... \n", "13583 [0.4271591007709503, 0.29361462593078613, 0.29... \n", "43843 [0.44168680906295776, 0.2790682315826416, 0.29... \n", "63589 [0.41863512992858887, 0.3106093108654022, 0.28... \n", "53695 [0.5650997757911682, 0.19236208498477936, 0.27... \n", "67529 [0.45511549711227417, 0.28582143783569336, 0.2... \n", "25493 [0.4276219606399536, 0.30413898825645447, 0.28... \n", "19486 [0.7149834036827087, 0.10459273308515549, 0.24... \n", "48449 [0.5363025665283203, 0.22163532674312592, 0.27... \n", "4504 [0.4641677737236023, 0.18824045360088348, 0.37... \n", "40285 [0.44292521476745605, 0.28201037645339966, 0.2... \n", "56741 [0.40168094635009766, 0.3777309060096741, 0.24... \n", "22948 [0.4365620017051697, 0.2830066680908203, 0.294... \n", "68426 [0.3452186584472656, 0.4593580365180969, 0.223... \n", "13431 [0.4436468482017517, 0.2736954987049103, 0.294... \n", "66287 [0.47334975004196167, 0.2881445586681366, 0.26... \n", "41980 [0.4635038673877716, 0.30251604318618774, 0.26... \n", "34632 [0.43297499418258667, 0.31000325083732605, 0.2... \n", "... ... \n", "47218 [0.470976859331131, 0.2997904419898987, 0.2489... \n", "35087 [0.4478594660758972, 0.30181050300598145, 0.27... \n", "61252 [0.5783949494361877, 0.15405726432800293, 0.30... \n", "39960 [0.4938848316669464, 0.24237176775932312, 0.25... \n", "365 [0.6203110218048096, 0.17863908410072327, 0.23... \n", "50665 [0.4619033932685852, 0.2977892756462097, 0.267... \n", "21007 [0.5029815435409546, 0.2711288630962372, 0.253... \n", "18819 [0.6974080801010132, 0.11416944861412048, 0.24... \n", "42421 [0.5765037536621094, 0.18341206014156342, 0.26... \n", "38705 [0.4030645787715912, 0.36543628573417664, 0.25... \n", "47033 [0.6219494342803955, 0.1795988380908966, 0.226... \n", "38742 [0.4049544930458069, 0.35102248191833496, 0.25... \n", "67610 [0.416861355304718, 0.35468244552612305, 0.245... \n", "61021 [0.4401417672634125, 0.2810788154602051, 0.292... \n", "14904 [0.5411785244941711, 0.2148503214120865, 0.246... \n", "50994 [0.7073097229003906, 0.12480126321315765, 0.23... \n", "6973 [0.6375303864479065, 0.14495858550071716, 0.24... \n", "3269 [0.5052645802497864, 0.21280284225940704, 0.30... \n", "54827 [0.5138391852378845, 0.26520460844039917, 0.25... \n", "19409 [0.520979106426239, 0.22392335534095764, 0.265... \n", "38703 [0.32009249925613403, 0.4819949269294739, 0.22... \n", "25133 [0.5703040361404419, 0.17875489592552185, 0.27... \n", "15339 [0.4571400284767151, 0.2949279546737671, 0.269... \n", "28082 [0.36291953921318054, 0.4310339391231537, 0.22... \n", "44462 [0.6122021675109863, 0.15579015016555786, 0.27... \n", "60212 [0.470508873462677, 0.2711063623428345, 0.2810... \n", "34950 [0.4431285262107849, 0.27477312088012695, 0.29... \n", "59462 [0.40375131368637085, 0.3289208710193634, 0.27... \n", "19465 [0.4494284391403198, 0.31076472997665405, 0.26... \n", "8135 [0.42708122730255127, 0.31392431259155273, 0.2... \n", "\n", "[2620 rows x 5 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(testlist)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* exactly correct labeled sentences:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.1851145038167939\n" ] } ], "source": [ "print (sum([1 if sample[1]['teacher'] == sample[1]['predict'] else 0 for sample in testlist.iterrows()]) / testlist.shape[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* mean squared error:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.02218282, 0.02594105, 0.00323429])" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "teacher_sentiments = np.array([sample[1]['teacher_sentiment'] for sample in testlist.iterrows()])\n", "predicted_sentiments = np.array([sample[1]['predicted_sentiment'] for sample in testlist.iterrows()])\n", "\n", "mean_squared_error = ((teacher_sentiments - predicted_sentiments)**2).mean(axis=0)\n", "display(mean_squared_error)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* by an overall variance of:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance teacher: [0.02183094 0.02513847 0.00285735]\n", "Variance prediction: [0.00850173 0.00793481 0.00095984]\n" ] } ], "source": [ "print(\"Variance teacher: \", np.var(teacher_sentiments, axis=0))\n", "print(\"Variance prediction: \", np.var(predicted_sentiments, axis=0))" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "testlist.to_csv('test.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* save classifier:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "import pickle\n", "clf.save(\"clf2.keras\")\n", "pickle.dump( vectorizer, open( \"vec2.pickle\", \"wb\" ) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "## testing area\n", "\n", "**for just testing, start from here!**\n", "\n", "download the dumped classifier and vectorizer from [here](https://the-cake-is-a-lie.net/nextcloud/index.php/s/NjMXamfwQsyrefG)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* loading classifier and vectorizer" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from IPython.display import clear_output, Markdown, Math\n", "import ipywidgets as widgets\n", "import sys\n", "sys.path.append(\"..\")\n", "\n", "from Tools.Emoji_Distance import sentiment_vector_to_emoji\n", "from Tools.Emoji_Distance import emoji_to_sentiment_vector\n", "\n", "def emoji2sent(emoji_arr):\n", " return np.array([emoji_to_sentiment_vector(e) for e in emoji_arr])\n", "\n", "def sent2emoji(sent_arr, custom_target_emojis=None):\n", " return [sentiment_vector_to_emoji(s, custom_target_emojis=custom_target_emojis) for s in sent_arr]" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import keras\n", "import pickle\n", "clf = keras.models.load_model(\"clf.keras\")\n", "vectorizer = pickle.load( open( \"vec.pickle\", \"rb\" ) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* define lookup emojis here:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "lookup_emojis = [#'πŸ˜‚',\n", " '😭',\n", " '😍',\n", " '😩',\n", " '😊',\n", " '😘',\n", " 'πŸ™',\n", " 'πŸ™Œ',\n", " 'πŸ˜‰',\n", " '😁',\n", " 'πŸ˜…',\n", " '😎',\n", " '😒',\n", " 'πŸ˜’',\n", " '😏',\n", " '😌',\n", " 'πŸ˜”',\n", " 'πŸ˜‹',\n", " 'πŸ˜€',\n", " '😀']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* a simple output widget for testing:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "755aa31644db4628a3be1ff3b621fa28", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Text(value='')" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4601eccc07074e71983c9005d1d329b1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(Button(description='get emoji', icon='check', style=ButtonStyle(), tooltip='Click me'), Output(…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "out = widgets.Output()\n", "\n", "t = widgets.Text()\n", "b = widgets.Button(\n", " description='get emoji',\n", " disabled=False,\n", " button_style='', # 'success', 'info', 'warning', 'danger' or ''\n", " tooltip='Click me',\n", " icon='check'\n", ")\n", "\n", "\n", "\n", "def handle_submit(sender):\n", " with out:\n", " clear_output()\n", " with out:\n", " pred = clf.predict(vectorizer.transform([t.value]))\n", " \n", " display(Markdown(\"# Predicted Emoji \" + str(sent2emoji(pred, lookup_emojis)[0])))\n", " display(Markdown(\"# Sentiment Vector: $$ \\pmatrix{\" + str(pred[0,0]) +\n", " \"\\\\\\\\\" + str(pred[0,1]) + \"\\\\\\\\\" + str(pred[0,2]) + \"}$$\"))\n", "\n", "b.on_click(handle_submit)\n", " \n", "display(t)\n", "display(widgets.VBox([b, out])) " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'mlb' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0my_trans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmlb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myt1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mpred_trans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmlb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myt1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'mlb' is not defined" ] } ], "source": [ "import numpy as np\n", "\n", "y_trans = mlb.inverse_transform(yt1)\n", "pred_trans = mlb.inverse_transform(yt1)\n", "\n", "# evaluate accuracy\n", "pos = 0\n", "neg = 0\n", "all = 0\n", "for entry in range(len(y_trans)):\n", " if len(np.intersect1d(y_trans[entry], pred_trans[entry])) > 0:\n", " pos += 1\n", " else:\n", " neg += 1\n", " all += 1\n", "print(pos/all)\n", "print(neg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }