POMS_sentiment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from thesaurus import Word\n",
    "from collections import defaultdict\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the dataset to obtain tweets from the time period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tweets(path=\"datasets/training_set_tweets.txt\"):\n",
    "    with open(path, encoding=\"utf8\") as f:\n",
    "        lines = f.readlines()\n",
    "        \n",
    "        tweets = []\n",
    "        dates  = []\n",
    "\n",
    "        split = lambda t: t.split('\\t')\n",
    "        \n",
    "        for line in map(split, lines):\n",
    "            if len(line) < 4 or len(line[3]) <= 10:\n",
    "                continue\n",
    "            try:\n",
    "                dates.append(datetime.strptime(line[3][:10], \"%Y-%m-%d\"))\n",
    "                tweets.append(line[2])\n",
    "            except ValueError:\n",
    "                continue\n",
    "            \n",
    "        \n",
    "    return tweets, dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3675242"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets, dates = get_tweets()\n",
    "len(tweets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's write the sentiment analysis algo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remember this takes ~1-2 min to run\n",
    "def get_poms_synonyms():\n",
    "    with open(\"poms.txt\") as f:\n",
    "        words = f.read().split(',')\n",
    "        \n",
    "        synonyms = {}\n",
    "        for word in words:\n",
    "            synonyms[word] = Word(word).synonyms()\n",
    "            \n",
    "    return synonyms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "poms_synonyms = get_poms_synonyms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def poms_scores(text, poms_synonyms):\n",
    "    raw_scores = defaultdict(int)\n",
    "    tokens = text.lower().split(' ')\n",
    "    \n",
    "    for token in tokens:\n",
    "        for word in poms_synonyms:\n",
    "            if token == word or token in poms_synonyms[word]:\n",
    "                raw_scores[word] += 1\n",
    "    \n",
    "    scores = {}\n",
    "    \n",
    "    scores['tension'] = raw_scores['tense']         + \\\n",
    "                        raw_scores['shaky']         + \\\n",
    "                        raw_scores['on edge']       + \\\n",
    "                        raw_scores['panicky']       + \\\n",
    "                       -raw_scores['relaxed']       + \\\n",
    "                        raw_scores['uneasy']        + \\\n",
    "                        raw_scores['restless']      + \\\n",
    "                        raw_scores['nervous']       + \\\n",
    "                        raw_scores['anxious']\n",
    "    \n",
    "    scores['depression'] = raw_scores['unhappy']    + \\\n",
    "                        raw_scores['sorry']         + \\\n",
    "                        raw_scores['sad']           + \\\n",
    "                        raw_scores['blue']          + \\\n",
    "                        raw_scores['hopeless']      + \\\n",
    "                        raw_scores['unworthy']      + \\\n",
    "                        raw_scores['discouraged']   + \\\n",
    "                        raw_scores['lonely']        + \\\n",
    "                        raw_scores['helpless']      + \\\n",
    "                        raw_scores['worthless']     + \\\n",
    "                        raw_scores['terrified']     + \\\n",
    "                        raw_scores['guilty']        \n",
    "    \n",
    "    scores['anger'] =   raw_scores['angry']         + \\\n",
    "                        raw_scores['peeved']        + \\\n",
    "                        raw_scores['grouchy']       + \\\n",
    "                        raw_scores['spiteful']      + \\\n",
    "                        raw_scores['annoyed']       + \\\n",
    "                        raw_scores['resentful']     + \\\n",
    "                        raw_scores['bitter']        + \\\n",
    "                        raw_scores['ready to fight']+ \\\n",
    "                        raw_scores['rebellious']    + \\\n",
    "                        raw_scores['deceived']      + \\\n",
    "                        raw_scores['furious']       + \\\n",
    "                        raw_scores['bad tempered']  \n",
    "    \n",
    "    scores['fatigue'] = raw_scores['worn out']      + \\\n",
    "                        raw_scores['listless']      + \\\n",
    "                        raw_scores['fatigued']      + \\\n",
    "                        raw_scores['exhausted']     + \\\n",
    "                        raw_scores['sluggish']      + \\\n",
    "                        raw_scores['weary']         + \\\n",
    "                        raw_scores['bushed']\n",
    "    \n",
    "    scores['confusion'] = raw_scores['confused']     + \\\n",
    "                        raw_scores['distracted']     + \\\n",
    "                        raw_scores['muddled']        + \\\n",
    "                        raw_scores['bewildered']     + \\\n",
    "                       -raw_scores['efficient']      + \\\n",
    "                        raw_scores['forgetful']      + \\\n",
    "                        raw_scores['uncertain']\n",
    "    \n",
    "    scores['vigour'] =  raw_scores['lively']         + \\\n",
    "                        raw_scores['active']         + \\\n",
    "                        raw_scores['energetic']      + \\\n",
    "                        raw_scores['cheerful']       + \\\n",
    "                        raw_scores['alert']          + \\\n",
    "                        raw_scores['peppy']          + \\\n",
    "                        raw_scores['carefree']       + \\\n",
    "                        raw_scores['vigorous']\n",
    "\n",
    "    \n",
    "    return scores\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example tweet (demonstrated emotion):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'...including his last failed relationship.  and while I know that none of it is grounded in reality, and I actually am worried about him...'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tension': 5,\n",
       " 'depression': 0,\n",
       " 'anger': 0,\n",
       " 'fatigue': 0,\n",
       " 'confusion': 0,\n",
       " 'vigour': 0}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poms_scores(tweets[9], poms_synonyms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we need to sort the Twitter dataset by day and plug each tweet into the POMS machine to get time series. Most of these functions will operate on millions of tweets so they'll probably take some time.\n",
    "\n",
    "In particular, the function below will take approximately 1 minute per 100,000 tweets processed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentiment_time_dict(tweets, dates, poms_synonyms):\n",
    "    sent_dict = defaultdict(lambda: {'tension': 0,\n",
    "                                     'depression': 0,\n",
    "                                     'anger': 0,\n",
    "                                     'fatigue': 0,\n",
    "                                     'confusion': 0,\n",
    "                                     'vigour': 0,\n",
    "                                     'total': 0})\n",
    "    start_date = datetime(2009, 9, 1)\n",
    "    discarded = 0.0\n",
    "    counter = 0\n",
    "    \n",
    "    for tweet, date in zip(tweets, dates):\n",
    "        counter += 1\n",
    "        if counter % 100000 == 0:\n",
    "            print(\"%d tweets processed.\" % counter)\n",
    "        \n",
    "        scores = poms_scores(tweet, poms_synonyms)\n",
    "        \n",
    "        days = (date - start_date).days\n",
    "        if days < 0:\n",
    "            discarded += 1\n",
    "            continue\n",
    "        \n",
    "        sent_dict[days]['tension'] += scores['tension']\n",
    "        sent_dict[days]['depression'] += scores['depression']\n",
    "        sent_dict[days]['anger'] += scores['anger']\n",
    "        sent_dict[days]['fatigue'] += scores['fatigue']\n",
    "        sent_dict[days]['confusion'] += scores['confusion']\n",
    "        sent_dict[days]['vigour'] += scores['vigour']\n",
    "        sent_dict[days]['total'] += 1\n",
    "        \n",
    "    for date in sent_dict:\n",
    "        sent_dict[date]['tension'] /= sent_dict[date]['total']\n",
    "        sent_dict[date]['depression'] /= sent_dict[date]['total']\n",
    "        sent_dict[date]['anger'] /= sent_dict[date]['total']\n",
    "        sent_dict[date]['fatigue'] /= sent_dict[date]['total']\n",
    "        sent_dict[date]['confusion'] /= sent_dict[date]['total']\n",
    "        sent_dict[date]['vigour'] /= sent_dict[date]['total']\n",
    "        \n",
    "    print(\"Dictionary construction complete. %f percent of tweets discarded.\" % (100 * discarded / len(tweets)) )\n",
    "    return dict(sent_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_sent_dict(path=\"sent_dict.txt\"):\n",
    "    with open(path, \"rb\") as f:\n",
    "        return pickle.load(f)\n",
    "\n",
    "def save_sent_dict(sent_dict, path=\"sent_dict.txt\"):\n",
    "    with open(path, \"wb\") as f:\n",
    "        pickle.dump(sent_dict, f, pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000 tweets processed.\n",
      "200000 tweets processed.\n",
      "300000 tweets processed.\n",
      "400000 tweets processed.\n",
      "500000 tweets processed.\n",
      "600000 tweets processed.\n",
      "700000 tweets processed.\n",
      "800000 tweets processed.\n",
      "900000 tweets processed.\n",
      "1000000 tweets processed.\n",
      "1100000 tweets processed.\n",
      "1200000 tweets processed.\n",
      "1300000 tweets processed.\n",
      "1400000 tweets processed.\n",
      "1500000 tweets processed.\n",
      "1600000 tweets processed.\n",
      "1700000 tweets processed.\n",
      "1800000 tweets processed.\n",
      "1900000 tweets processed.\n",
      "2000000 tweets processed.\n",
      "2100000 tweets processed.\n",
      "2200000 tweets processed.\n",
      "2300000 tweets processed.\n",
      "2400000 tweets processed.\n",
      "2500000 tweets processed.\n",
      "2600000 tweets processed.\n",
      "2700000 tweets processed.\n",
      "2800000 tweets processed.\n",
      "2900000 tweets processed.\n",
      "3000000 tweets processed.\n",
      "3100000 tweets processed.\n",
      "3200000 tweets processed.\n",
      "3300000 tweets processed.\n",
      "3400000 tweets processed.\n",
      "3500000 tweets processed.\n",
      "3600000 tweets processed.\n",
      "Dictionary construction complete. 10.478412 percent of tweets discarded.\n"
     ]
    }
   ],
   "source": [
    "sent_dict = sentiment_time_dict(tweets, dates, poms_synonyms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This cell should be used for saving and loading sent_dict as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sent_dict = load_sent_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentiment_time_series(sent_dict, key):\n",
    "    N = max(sent_dict.keys())\n",
    "    \n",
    "    T = np.ndarray((N,))\n",
    "    for i in range(N):\n",
    "        T[i] = sent_dict[i][key]\n",
    "        \n",
    "    #convert to z-scores\n",
    "    mean = np.mean(T)\n",
    "    std  = np.std(T)\n",
    "    T = (T - mean) / std\n",
    "        \n",
    "    return T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "series = sentiment_time_series(sent_dict, 'vigour') - sentiment_time_series(sent_dict, 'depression')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are finally able to view the results of our time series. Below is a happiness score around Thanksgiving, which occurs at $x=6$ on the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
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       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22b20086240>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.title(\"Happiness\")\n",
    "dfft = np.fft.fft(series)\n",
    "dfft[20:] = 0\n",
    "new_series = np.real(np.fft.ifft(dfft))\n",
    "#plt.axvline(x=60, label=\"Halloween\", c=\"red\")\n",
    "#plt.axvline(x=86, label=\"Thanksgiving\", c=\"green\")\n",
    "ax.plot(new_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_sent_dict(sent_dict, path=\"sent_dict_smooth.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"poms_synonyms.txt\", \"wb\") as f:\n",
    "    pickle.dump(poms_synonyms, f, pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "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
}