diff --git a/SentimentAnalysis_RNN.ipynb b/SentimentAnalysis_RNN.ipynb
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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "efyTtfc_oJkh"
+      },
+      "source": [
+        "## 1. Adding imports & installing neccessay packages ##"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GaVIBWlyoKz3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1fbc85d8-fc75-4df9-dd99-b9d2968fcf46"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/gdrive\n"
+          ]
+        }
+      ],
+      "source": [
+        "### run this if using google colab to mount google drive as local storage\n",
+        "\n",
+        "from google.colab import drive\n",
+        "import os\n",
+        "drive.mount('/content/gdrive')\n",
+        "\n",
+        "repo_path = '/content/gdrive/My Drive/colab/NLP-Bootcamp/'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sdBgdze84r8s"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import collections\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# Import modules to calculate accuracy and confusion matrix\n",
+        "from sklearn.metrics import confusion_matrix, accuracy_score"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YLttTMckfNa_"
+      },
+      "source": [
+        "## 2. Loading Data ##"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "w9EA4jMv4ywO",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "4590a838-52eb-45f6-b056-bb8685147946"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  target         ids             user  \\\n",
+              "0      p  1978186076   ceruleanbreeze   \n",
+              "1      p  1994697891  enthusiasticjen   \n",
+              "2      p  2191885992      LifeRemixed   \n",
+              "3      p  1753662211        lovemandy   \n",
+              "4      p  2177442789      _LOVELYmanu   \n",
+              "\n",
+              "                                                text  \n",
+              "0  @nocturnalie Anyway, and now Abby and I share ...  \n",
+              "1  @JoeGigantino Few times I'm trying to leave co...  \n",
+              "2  @AngieGriffin Good Morning Angie  I'll be in t...  \n",
+              "3  had a good day driving up mountains, visiting ...  \n",
+              "4          downloading some songs  i love lady GaGa.  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-07dd2b8e-21c9-4775-81ac-02f7205f2d38\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>target</th>\n",
+              "      <th>ids</th>\n",
+              "      <th>user</th>\n",
+              "      <th>text</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>p</td>\n",
+              "      <td>1978186076</td>\n",
+              "      <td>ceruleanbreeze</td>\n",
+              "      <td>@nocturnalie Anyway, and now Abby and I share ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>p</td>\n",
+              "      <td>1994697891</td>\n",
+              "      <td>enthusiasticjen</td>\n",
+              "      <td>@JoeGigantino Few times I'm trying to leave co...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>p</td>\n",
+              "      <td>2191885992</td>\n",
+              "      <td>LifeRemixed</td>\n",
+              "      <td>@AngieGriffin Good Morning Angie  I'll be in t...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>p</td>\n",
+              "      <td>1753662211</td>\n",
+              "      <td>lovemandy</td>\n",
+              "      <td>had a good day driving up mountains, visiting ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>p</td>\n",
+              "      <td>2177442789</td>\n",
+              "      <td>_LOVELYmanu</td>\n",
+              "      <td>downloading some songs  i love lady GaGa.</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07dd2b8e-21c9-4775-81ac-02f7205f2d38')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-07dd2b8e-21c9-4775-81ac-02f7205f2d38 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-07dd2b8e-21c9-4775-81ac-02f7205f2d38');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ],
+      "source": [
+        "### run below 2 lines of code for setting train & test data path on google colab\n",
+        "trainData = os.path.join(repo_path, 'data/sentiment140_160k_tweets_train.csv')\n",
+        "testData = os.path.join(repo_path, 'data/sentiment140_test.csv')\n",
+        "\n",
+        "### run below 3 lines of code for setting train & test data path on local machine\n",
+        "'''\n",
+        "DATA = './data/'\n",
+        "trainData = DATA + 'sentiment140_160k_tweets_train.csv'\n",
+        "testData =  DATA + 'sentiment140_test.csv'\n",
+        "'''\n",
+        "\n",
+        "train = pd.read_csv(trainData)\n",
+        "test = pd.read_csv(testData)\n",
+        "\n",
+        "train.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DE0NVFR9s4o4"
+      },
+      "source": [
+        "Looking at distribution of *'positives'* & *'negatives'* samples in train dataset "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MF2-MSXFoJkr",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1ba63640-3a77-48e5-f620-11b8260ada21"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Counter({'p': 80000, 'n': 79985})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ],
+      "source": [
+        "collections.Counter(train['target'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "vwyLXx_moJks",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 293
+        },
+        "outputId": "aceb2fa0-4236-46ab-a950-d6eb656c9cbb"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff720070f50>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ],
+      "source": [
+        "train.groupby('target').size().plot(kind='bar')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xyHV7gCCxCpO"
+      },
+      "source": [
+        "We will find that it is a relatively well-balanced dataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5RK3QPUnbUFq"
+      },
+      "source": [
+        "## 3. Data (Text) Preprocessing ##"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "N_p8SQxQMKrq"
+      },
+      "outputs": [],
+      "source": [
+        "### mapping a dictionary of apostrophe words\n",
+        "\n",
+        "appos = {\n",
+        "\"aren't\" : \"are not\",\n",
+        "\"can't\" : \"cannot\",\n",
+        "\"cant\" : \"cannot\",\n",
+        "\"couldn't\" : \"could not\",\n",
+        "\"didn't\" : \"did not\",\n",
+        "\"doesn't\" : \"does not\",\n",
+        "\"don't\" : \"do not\",\n",
+        "\"hadn't\" : \"had not\",\n",
+        "\"hasn't\" : \"has not\",\n",
+        "\"haven't\" : \"have not\",\n",
+        "\"he'd\" : \"he would\",\n",
+        "\"he'll\" : \"he will\",\n",
+        "\"he's\" : \"he is\",\n",
+        "\"i'd\" : \"I would\",\n",
+        "\"i'd\" : \"I had\",\n",
+        "\"i'll\" : \"I will\",\n",
+        "\"i'm\" : \"I am\",\n",
+        "\"im\" : \"I am\",\n",
+        "\"isn't\" : \"is not\",\n",
+        "\"it's\" : \"it is\",\n",
+        "\"it'll\":\"it will\",\n",
+        "\"i've\" : \"I have\",\n",
+        "\"let's\" : \"let us\",\n",
+        "\"mightn't\" : \"might not\",\n",
+        "\"mustn't\" : \"must not\",\n",
+        "\"shan't\" : \"shall not\",\n",
+        "\"she'd\" : \"she would\",\n",
+        "\"she'll\" : \"she will\",\n",
+        "\"she's\" : \"she is\",\n",
+        "\"shouldn't\" : \"should not\",\n",
+        "\"that's\" : \"that is\",\n",
+        "\"there's\" : \"there is\",\n",
+        "\"they'd\" : \"they would\",\n",
+        "\"they'll\" : \"they will\",\n",
+        "\"they're\" : \"they are\",\n",
+        "\"they've\" : \"they have\",\n",
+        "\"we'd\" : \"we would\",\n",
+        "\"we're\" : \"we are\",\n",
+        "\"weren't\" : \"were not\",\n",
+        "\"we've\" : \"we have\",\n",
+        "\"what'll\" : \"what will\",\n",
+        "\"what're\" : \"what are\",\n",
+        "\"what's\" : \"what is\",\n",
+        "\"what've\" : \"what have\",\n",
+        "\"where's\" : \"where is\",\n",
+        "\"who'd\" : \"who would\",\n",
+        "\"who'll\" : \"who will\",\n",
+        "\"who're\" : \"who are\",\n",
+        "\"who's\" : \"who is\",\n",
+        "\"who've\" : \"who have\",\n",
+        "\"won't\" : \"will not\",\n",
+        "\"wouldn't\" : \"would not\",\n",
+        "\"you'd\" : \"you would\",\n",
+        "\"you'll\" : \"you will\",\n",
+        "\"you're\" : \"you are\",\n",
+        "\"you've\" : \"you have\",\n",
+        "\"'re\": \" are\",\n",
+        "\"wasn't\": \"was not\",\n",
+        "\"we'll\":\" will\",\n",
+        "\"didn't\": \"did not\",\n",
+        "\"gg\" : \"going\"\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7IbqS4m-4-EX",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "38904185-6b96-49f0-a68c-05012f58de30"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  target         ids             user  \\\n",
+              "0      p  1978186076   ceruleanbreeze   \n",
+              "1      p  1994697891  enthusiasticjen   \n",
+              "2      p  2191885992      LifeRemixed   \n",
+              "3      p  1753662211        lovemandy   \n",
+              "4      p  2177442789      _LOVELYmanu   \n",
+              "\n",
+              "                                                text  \\\n",
+              "0  @nocturnalie Anyway, and now Abby and I share ...   \n",
+              "1  @JoeGigantino Few times I'm trying to leave co...   \n",
+              "2  @AngieGriffin Good Morning Angie  I'll be in t...   \n",
+              "3  had a good day driving up mountains, visiting ...   \n",
+              "4          downloading some songs  i love lady GaGa.   \n",
+              "\n",
+              "                                                 ugc  \n",
+              "0  anyway and now abby and i share all our crops ...  \n",
+              "1  few times I am trying to leave comments in you...  \n",
+              "2  good morning angie I will be in the atl july 8...  \n",
+              "3  had a good day driving up mountains visiting k...  \n",
+              "4            downloading some songs i love lady gaga  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7f072219-9efb-4309-b32b-2539badf7cd6\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>target</th>\n",
+              "      <th>ids</th>\n",
+              "      <th>user</th>\n",
+              "      <th>text</th>\n",
+              "      <th>ugc</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>p</td>\n",
+              "      <td>1978186076</td>\n",
+              "      <td>ceruleanbreeze</td>\n",
+              "      <td>@nocturnalie Anyway, and now Abby and I share ...</td>\n",
+              "      <td>anyway and now abby and i share all our crops ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>p</td>\n",
+              "      <td>1994697891</td>\n",
+              "      <td>enthusiasticjen</td>\n",
+              "      <td>@JoeGigantino Few times I'm trying to leave co...</td>\n",
+              "      <td>few times I am trying to leave comments in you...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>p</td>\n",
+              "      <td>2191885992</td>\n",
+              "      <td>LifeRemixed</td>\n",
+              "      <td>@AngieGriffin Good Morning Angie  I'll be in t...</td>\n",
+              "      <td>good morning angie I will be in the atl july 8...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>p</td>\n",
+              "      <td>1753662211</td>\n",
+              "      <td>lovemandy</td>\n",
+              "      <td>had a good day driving up mountains, visiting ...</td>\n",
+              "      <td>had a good day driving up mountains visiting k...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>p</td>\n",
+              "      <td>2177442789</td>\n",
+              "      <td>_LOVELYmanu</td>\n",
+              "      <td>downloading some songs  i love lady GaGa.</td>\n",
+              "      <td>downloading some songs i love lady gaga</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7f072219-9efb-4309-b32b-2539badf7cd6')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-7f072219-9efb-4309-b32b-2539badf7cd6 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-7f072219-9efb-4309-b32b-2539badf7cd6');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ],
+      "source": [
+        "import re\n",
+        "\n",
+        "def preprocess_text(sentence):\n",
+        "    text = re.sub('((www\\.[^\\s]+)|(https?://[^\\s]+))','', sentence['text'])\n",
+        "    text = re.sub('@[^\\s]+','', text)\n",
+        "    text = text.lower().split()\n",
+        "    reformed = [appos[word] if word in appos else word for word in text]\n",
+        "    reformed = \" \".join(reformed) \n",
+        "    text = re.sub('&[^\\s]+;', '', reformed)\n",
+        "    text = re.sub('[^a-zA-Zа-яА-Я1-9]+', ' ', text)\n",
+        "    text = re.sub(' +',' ', text)\n",
+        "    #text = re.sub(' [\\w] ', ' ', text)\n",
+        "    return text.strip()\n",
+        "\n",
+        "preprocess = train\n",
+        "preprocess['ugc'] = preprocess.apply(preprocess_text, axis=1)\n",
+        "\n",
+        "preprocess.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CUsyZDEqK_4r"
+      },
+      "source": [
+        "## 4. Sentiment Analysis using Deep Learning-based Method: RNN ##"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ReMymperLMAh"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import classification_report\n",
+        "from keras.preprocessing.text import Tokenizer\n",
+        "from keras.preprocessing import sequence\n",
+        "\n",
+        "max_features = 4000\n",
+        "#num_words = 20\n",
+        "embedding_size = 256\n",
+        "lstm_dim = 256\n",
+        "batch_size = 64\n",
+        "num_epochs = 10\n",
+        "\n",
+        "# Create tokenizer by converting text into sequence of integers\n",
+        "tokenizer = Tokenizer(num_words=max_features, split=' ')\n",
+        "tokenizer.fit_on_texts(preprocess['ugc'].values)\n",
+        "\n",
+        "X = tokenizer.texts_to_sequences(preprocess['ugc'].values)\n",
+        "X = sequence.pad_sequences(X)\n",
+        "#X = sequence.pad_sequences(X, maxlen=num_words)\n",
+        "y = pd.get_dummies(preprocess['target']).values\n",
+        "\n",
+        "# Adding 1 because of reserved 0 index\n",
+        "vocab_size = len(tokenizer.word_index) + 1"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fqZyya_Y6xhl",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "44c5cbd1-51bb-45c9-f107-45b513c341a1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
+            "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " embedding (Embedding)       (None, 38, 256)           1024000   \n",
+            "                                                                 \n",
+            " spatial_dropout1d (SpatialD  (None, 38, 256)          0         \n",
+            " ropout1D)                                                       \n",
+            "                                                                 \n",
+            " lstm (LSTM)                 (None, 38, 256)           525312    \n",
+            "                                                                 \n",
+            " lstm_1 (LSTM)               (None, 128)               197120    \n",
+            "                                                                 \n",
+            " dense (Dense)               (None, 2)                 258       \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 1,746,690\n",
+            "Trainable params: 1,746,690\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Define model\n",
+        "from keras import Sequential\n",
+        "from keras.layers import Embedding, LSTM, Dropout, Dense, SpatialDropout1D\n",
+        "\n",
+        "model = Sequential()\n",
+        "model.add(Embedding(input_dim=max_features,\n",
+        "                    output_dim=embedding_size,\n",
+        "                    input_length=X.shape[1]))\n",
+        "model.add(SpatialDropout1D(0.4))\n",
+        "model.add(LSTM(units=lstm_dim,\n",
+        "               dropout=0.2,\n",
+        "               activation='tanh',\n",
+        "               recurrent_dropout=0.2,\n",
+        "               recurrent_activation='sigmoid',\n",
+        "               return_sequences=True))\n",
+        "model.add(LSTM(units=128,\n",
+        "               dropout=0.2,\n",
+        "               activation='tanh',\n",
+        "               recurrent_dropout=0.2,\n",
+        "               recurrent_activation='sigmoid'))\n",
+        "model.add(Dense(2, activation='sigmoid')) \n",
+        "\n",
+        "model.summary()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HRNFpdoUN3Cw"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile model\n",
+        "model.compile(loss='binary_crossentropy',\n",
+        "              optimizer='adam',\n",
+        "              metrics=[['accuracy']])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ogRmCK1UZhCC"
+      },
+      "outputs": [],
+      "source": [
+        "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size = 0.2, shuffle=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AJ083iTdOKNo",
+        "outputId": "eaf7b1cc-040a-4e3d-84eb-f8ad1e77ddf6"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/10\n",
+            "2000/2000 [==============================] - 699s 346ms/step - loss: 0.4805 - accuracy: 0.7679 - val_loss: 0.4439 - val_accuracy: 0.7926\n",
+            "Epoch 2/10\n",
+            "2000/2000 [==============================] - 702s 351ms/step - loss: 0.4287 - accuracy: 0.8005 - val_loss: 0.4355 - val_accuracy: 0.7964\n",
+            "Epoch 3/10\n",
+            "2000/2000 [==============================] - 690s 345ms/step - loss: 0.4076 - accuracy: 0.8133 - val_loss: 0.4422 - val_accuracy: 0.7967\n",
+            "Epoch 4/10\n",
+            "2000/2000 [==============================] - 684s 342ms/step - loss: 0.3895 - accuracy: 0.8233 - val_loss: 0.4425 - val_accuracy: 0.7979\n",
+            "Epoch 5/10\n",
+            "2000/2000 [==============================] - 677s 339ms/step - loss: 0.3737 - accuracy: 0.8324 - val_loss: 0.4458 - val_accuracy: 0.7996\n",
+            "Epoch 6/10\n",
+            "2000/2000 [==============================] - 674s 337ms/step - loss: 0.3560 - accuracy: 0.8405 - val_loss: 0.4561 - val_accuracy: 0.7929\n",
+            "Epoch 7/10\n",
+            "2000/2000 [==============================] - 674s 337ms/step - loss: 0.3394 - accuracy: 0.8485 - val_loss: 0.4660 - val_accuracy: 0.7936\n",
+            "Epoch 8/10\n",
+            "2000/2000 [==============================] - 676s 338ms/step - loss: 0.3208 - accuracy: 0.8588 - val_loss: 0.4931 - val_accuracy: 0.7899\n",
+            "Epoch 9/10\n",
+            "2000/2000 [==============================] - 678s 339ms/step - loss: 0.3037 - accuracy: 0.8671 - val_loss: 0.5225 - val_accuracy: 0.7865\n",
+            "Epoch 10/10\n",
+            "2000/2000 [==============================] - 674s 337ms/step - loss: 0.2876 - accuracy: 0.8745 - val_loss: 0.5419 - val_accuracy: 0.7852\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Fit model\n",
+        "history = model.fit(Xtrain, ytrain, batch_size=batch_size, epochs=num_epochs, validation_data=(Xtest, ytest))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Dft2-xIgnjCI",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d0b90103-126f-473b-af21-0a3bfc00d373"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy: 78.52%\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Final evaluation of the model\n",
+        "scores = model.evaluate(Xtest, ytest, verbose=0)\n",
+        "print(\"Accuracy: %.2f%%\" % (scores[1]*100))"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [],
+      "provenance": []
+    },
+    "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": "3.7.3"
+    },
+    "gpuClass": "standard"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file