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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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"image/png": "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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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"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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"image/png": "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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
}
