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\n",
    "import matplotlib\n"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rc('font', family='symbola', size=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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(figsize=(15,10))\n",
    "    plt.title(title + \" -- sentiment-plot\")\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.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",
    "    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",
    "    #plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "130af70105914e61a07d14248df3d114",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6a1d7210b874215bb8d0e576b4ca133",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "26d17ff521604443ab1da9ff63d1108a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "188c871e705040c1b17bca0e10d5c5fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}