256 lines
7.1 KiB
Plaintext
256 lines
7.1 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Emoji Distance\n",
|
|
"a notebook dealing witch emoji distance measures. Uses an external csv with labeled data to compare arbitriary emojis related to sentiment\n",
|
|
"Autor = Carsten Draschner\n",
|
|
"Version = 0.1\n",
|
|
"## Used Ressources\n",
|
|
"https://www.clarin.si/repository/xmlui/handle/11356/1048\n",
|
|
"https://github.com/words/emoji-emotion"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import pandas as pd\n",
|
|
"import math\n",
|
|
"import numpy as np"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#read in csv as panda file\n",
|
|
"df = pd.read_csv(\"/Users/Carsten/GitRepos/NLP-LAB/Project/Tools/Emoji_Sentiment_Data_v1.0.csv\", delimiter=\";\")\n",
|
|
"#df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#calculates vector distance between 2 3-dim sentiment representations of emojis\n",
|
|
"def sentiment_vector_dist(v1,v2):\n",
|
|
" #pos_v1 = v1[0]\n",
|
|
" #neg_v1 = v1[1]\n",
|
|
" #neu_v1 = v1[2]\n",
|
|
" \n",
|
|
" #pos_v2 = v2[0]\n",
|
|
" #neg_v2 = v2[1]\n",
|
|
" #neu_v2 = v2[2]\n",
|
|
" \n",
|
|
" #tmp_dist = float(np.abs(pos_v1-pos_v2))+float(np.abs(neg_v1-neg_v2))+float(np.abs(neu_v1-neu_v2))\n",
|
|
" \n",
|
|
" #calculates vector distance between 2 3-dim sentiment representations of emojis consisting of positive neutral and negative probabilistic occuring\n",
|
|
" tmp_dist = np.linalg.norm(np.array(v1)-np.array(v2)) \n",
|
|
" return tmp_dist"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#calculates vector representation in a 3dim 0 to 1space of dimension: positive,negative,neutral\n",
|
|
"def emoji_to_sentiment_vector(e):\n",
|
|
" tmp = df[df[\"Emoji\"]==e] \n",
|
|
" #calculate by espacial labeled occurences devided by sum of overall occurences\n",
|
|
" pos = tmp[\"Positive\"].values/tmp[\"Occurrences\"].values\n",
|
|
" neg = tmp[\"Negative\"].values/tmp[\"Occurrences\"].values\n",
|
|
" neu = tmp[\"Neutral\"].values/tmp[\"Occurrences\"].values\n",
|
|
" #return as np array\n",
|
|
" return np.array([pos,neg,neu])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#function to call for evaluating two emojis in its sentimental distance\n",
|
|
"def emoji_distance(e1,e2):\n",
|
|
" sent_v1 = emoji_to_sentiment_vector(e1)\n",
|
|
" sent_v2 = emoji_to_sentiment_vector(e2)\n",
|
|
" \n",
|
|
" d = sentiment_vector_dist(sent_v1,sent_v2)\n",
|
|
" return d"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 42,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def sentiment_vector_to_emoji(v1):\n",
|
|
" #if(len(v1) == 3):\n",
|
|
" #set initial values to compare with\n",
|
|
" best_emoji = \"😐\"\n",
|
|
" min_distance = 10000\n",
|
|
" \n",
|
|
" #compare only with filtred emoticons\n",
|
|
" df_filtered = df[df[\"Unicode block\"]==\"Emoticons\"]\n",
|
|
" all_smilies = list(df_filtered[\"Emoji\"])\n",
|
|
" for e in all_smilies:\n",
|
|
" v2 = emoji_to_sentiment_vector(e)\n",
|
|
" d = sentiment_vector_dist(v1,v2)\n",
|
|
" if(d < min_distance):\n",
|
|
" min_distance = d\n",
|
|
" best_emoji = e\n",
|
|
" print(str(v1),str(v2),str(min_distance),str(type(v1)),str(type(v2)),e)\n",
|
|
"\n",
|
|
"\n",
|
|
" print(\"for sentiment vector: \"+str(v1)+\" the emoji is : \"+str(best_emoji)+\" with distance of \"+str(min_distance)+\"!\")\n",
|
|
" return best_emoji\n",
|
|
" \n",
|
|
" #else:\n",
|
|
" #print(\"WRONG SENTIMENT VECTOR\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def show_demo():\n",
|
|
" df_filtered = df[df[\"Unicode block\"]==\"Emoticons\"]\n",
|
|
" all_smilies = list(df_filtered[\"Emoji\"])\n",
|
|
"\n",
|
|
" d_m = np.zeros(shape=(len(all_smilies),len(all_smilies)))\n",
|
|
"\n",
|
|
" for c1 in range(len(all_smilies)):\n",
|
|
" for c2 in range(len(all_smilies)):\n",
|
|
" e1 = all_smilies[c1]\n",
|
|
" e2 = all_smilies[c2]\n",
|
|
"\n",
|
|
" d = emoji_distance(e1,e2)\n",
|
|
" d_m[c1,c2] = d\n",
|
|
" \n",
|
|
" for c in range(len(d_m[0])):\n",
|
|
" emoji = all_smilies[c]\n",
|
|
" row = d_m[c]\n",
|
|
" row_sorted = np.argsort(row)\n",
|
|
" #closest 5\n",
|
|
" r = row_sorted[0:10]\n",
|
|
" #print()\n",
|
|
" closest = \"\"\n",
|
|
" for i in r:\n",
|
|
" closest+=all_smilies[i]\n",
|
|
" print(emoji+\": \"+closest)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[ 0.39118825]\n",
|
|
" [ 0.38451268]\n",
|
|
" [ 0.22429907]] [[ 0.46813021]\n",
|
|
" [ 0.24716181]\n",
|
|
" [ 0.28470797]] 0.168625514858 <class 'numpy.ndarray'> <class 'numpy.ndarray'> 😂\n",
|
|
"[[ 0.39118825]\n",
|
|
" [ 0.38451268]\n",
|
|
" [ 0.22429907]] [[ 0.34310532]\n",
|
|
" [ 0.43648208]\n",
|
|
" [ 0.2204126 ]] 0.0709076267317 <class 'numpy.ndarray'> <class 'numpy.ndarray'> 😭\n",
|
|
"[[ 0.39118825]\n",
|
|
" [ 0.38451268]\n",
|
|
" [ 0.22429907]] [[ 0.39118825]\n",
|
|
" [ 0.38451268]\n",
|
|
" [ 0.22429907]] 0.0 <class 'numpy.ndarray'> <class 'numpy.ndarray'> 😢\n",
|
|
"for sentiment vector: [[ 0.39118825]\n",
|
|
" [ 0.38451268]\n",
|
|
" [ 0.22429907]] the emoji is : 😢 with distance of 0.0!\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'😢'"
|
|
]
|
|
},
|
|
"execution_count": 46,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"#show_demo()\n",
|
|
"v11 = emoji_to_sentiment_vector(\"😢\")\n",
|
|
"sentiment_vector_to_emoji(v11)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"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": "3.6.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|