nlp-lab/Project/Tools/emoji_plotting.ipynb

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{
"cells": [
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
"outputs": [],
"source": [
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"%matplotlib inline\n",
"#widget"
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]
},
{
"cell_type": "code",
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"execution_count": 5,
"metadata": {
"collapsed": true
},
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"outputs": [],
"source": [
"def sentiment_score(s):\n",
" #(pos, neg, neu)^T\n",
" return s[0] - s[1]"
]
},
{
"cell_type": "code",
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"execution_count": 6,
"metadata": {
"collapsed": true
},
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"outputs": [],
"source": [
"import Emoji_Distance as edist\n",
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"import numpy as np\n",
"from __future__ import unicode_literals\n",
"\n",
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"import matplotlib.pyplot as plt\n",
"import matplotlib\n"
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]
},
{
"cell_type": "code",
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"execution_count": 7,
"metadata": {
"collapsed": true
},
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"outputs": [],
"source": [
"list_emoticon_scores = np.array([sentiment_score(x) for x in edist.list_sentiment_emoticon_vectors])"
]
},
{
"cell_type": "code",
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"execution_count": 8,
"metadata": {
"collapsed": true
},
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"outputs": [],
"source": [
"matplotlib.rc('font', family='symbola', size=16)"
]
},
{
"cell_type": "code",
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"execution_count": 12,
"metadata": {
"collapsed": true
},
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"outputs": [],
"source": [
"def plot_emoji_list(sentiment_vectors, unicode_repr, title=\"Emoji Space\"):\n",
" \n",
" # sentiment score axis\n",
" X = np.array([sentiment_score(x) for x in sentiment_vectors])\n",
" \n",
" # neutral axis:\n",
" Y = np.array([x[2] for x in sentiment_vectors])\n",
" \n",
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" fig_1, ax_1 = plt.subplots(figsize=(15,10))\n",
" plt.title(title + \" -- sentiment-plot\")\n",
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" plt.xlabel(\"sentiment score\")\n",
" plt.ylabel(\"neutrality\")\n",
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" plt.xlim([-0.4,0.9])\n",
" plt.ylim([0.07,0.6])\n",
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" for i in range(len(X)):\n",
" plt.text(X[i], Y[i], unicode_repr[i])\n",
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" plt.savefig(title + \" -- sentiment-plot.png\", bbox_inches='tight')\n",
" \n",
" # sentiment score axis\n",
" X = np.array([x[0] for x in sentiment_vectors])\n",
" \n",
" # neutral axis:\n",
" Y = np.array([x[1] for x in sentiment_vectors])\n",
" \n",
" fig_2, ax_2 = plt.subplots(figsize=(15,10))\n",
" plt.title(title + \" -- positive-negative-plot\")\n",
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" plt.xlim([0.15,0.85])\n",
" plt.ylim([0.0,0.7])\n",
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" plt.xlabel(\"positive\")\n",
" plt.ylabel(\"negative\")\n",
" for i in range(len(X)):\n",
" plt.text(X[i], Y[i], unicode_repr[i])\n",
" plt.savefig(title + \" -- positive-negative-plot.png\", bbox_inches='tight')\n",
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" #plt.show()\n",
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" "
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]
},
{
"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
"outputs": [
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{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/Carsten/anaconda3/lib/python3.6/site-packages/matplotlib/font_manager.py:1316: UserWarning: findfont: Font family ['symbola'] not found. Falling back to DejaVu Sans\n",
" (prop.get_family(), self.defaultFamily[fontext]))\n"
]
},
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{
"data": {
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"text/plain": [
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"<matplotlib.figure.Figure at 0x11eb967f0>"
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]
},
"metadata": {},
"output_type": "display_data"
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},
{
"data": {
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"image/png": "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"text/plain": [
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"<matplotlib.figure.Figure at 0x11ec289b0>"
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]
},
"metadata": {},
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"output_type": "display_data"
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}
],
"source": [
"plot_emoji_list(sentiment_vectors=edist.list_sentiment_emoticon_vectors, unicode_repr=edist.list_emoticon_emojis, title=\"Emoticon Emoji space\")"
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/Carsten/anaconda3/lib/python3.6/site-packages/matplotlib/font_manager.py:1316: UserWarning: findfont: Font family ['symbola'] not found. Falling back to DejaVu Sans\n",
" (prop.get_family(), self.defaultFamily[fontext]))\n"
]
},
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{
"data": {
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"image/png": "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"text/plain": [
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"<matplotlib.figure.Figure at 0x10e9869e8>"
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<matplotlib.figure.Figure at 0x114d06320>"
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]
},
"metadata": {},
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"output_type": "display_data"
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}
],
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"source": [
"plot_emoji_list(sentiment_vectors=edist.list_sentiment_vectors, unicode_repr=edist.list_emojis, title=\"Full Emoji space\")"
]
},
{
"cell_type": "code",
"execution_count": null,
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"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
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"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",
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"version": "3.6.3"
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}
},
"nbformat": 4,
"nbformat_minor": 2
}