nlp-lab/Project/Tools/emoji_plotting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentiment_score(s):\n",
    "    #(pos, neg, neu)^T\n",
    "    return s[0] - s[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Emoji_Distance as edist\n",
    "import numpy as np\n",
    "from __future__ import unicode_literals\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_emoticon_scores = np.array([sentiment_score(x) for x in edist.list_sentiment_emoticon_vectors])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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",
    "    fig_1, ax_1 = plt.subplots()\n",
    "    plt.title(title)\n",
    "    plt.xlabel(\"sentiment score\")\n",
    "    plt.ylabel(\"neutrality\")\n",
    "    plt.xlim([-1,1])\n",
    "    for i in range(len(X)):\n",
    "        plt.text(X[i], Y[i], unicode_repr[i])\n",
    "    #plt.show()\n",
    "    return fig_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ef9841e1ef94fdd8606e244eec54164",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_emoji_list(sentiment_vectors=edist.list_sentiment_emoticon_vectors, unicode_repr=edist.list_emoticon_emojis, title=\"Emoticon Emoji space\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7a14835ea00d4401b0fd2d6b7b65957f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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,
   "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"
  }
 },
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 "nbformat_minor": 2
}