01_data_cleaning.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Data cleaning\n",
    "During our workshops we are going to use a public dataset of tweets, which have been already labelled with sentiment.\n",
    "\n",
    "https://www.kaggle.com/crowdflower/twitter-airline-sentiment"
   ]
  },
  {
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     "end_time": "2019-12-03T09:43:18.768837Z",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>airline_sentiment</th>\n",
       "      <th>airline_sentiment_confidence</th>\n",
       "      <th>negativereason</th>\n",
       "      <th>negativereason_confidence</th>\n",
       "      <th>airline</th>\n",
       "      <th>airline_sentiment_gold</th>\n",
       "      <th>name</th>\n",
       "      <th>negativereason_gold</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_coord</th>\n",
       "      <th>tweet_created</th>\n",
       "      <th>tweet_location</th>\n",
       "      <th>user_timezone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>570306133677760513</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cairdin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:35:52 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>570301130888122368</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:59 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>570301083672813571</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yvonnalynn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:48 -0800</td>\n",
       "      <td>Lets Play</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>570301031407624196</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Bad Flight</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:36 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>570300817074462722</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:14:45 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id airline_sentiment  airline_sentiment_confidence  \\\n",
       "0  570306133677760513           neutral                        1.0000   \n",
       "1  570301130888122368          positive                        0.3486   \n",
       "2  570301083672813571           neutral                        0.6837   \n",
       "3  570301031407624196          negative                        1.0000   \n",
       "4  570300817074462722          negative                        1.0000   \n",
       "\n",
       "  negativereason  negativereason_confidence         airline  \\\n",
       "0            NaN                        NaN  Virgin America   \n",
       "1            NaN                     0.0000  Virgin America   \n",
       "2            NaN                        NaN  Virgin America   \n",
       "3     Bad Flight                     0.7033  Virgin America   \n",
       "4     Can't Tell                     1.0000  Virgin America   \n",
       "\n",
       "  airline_sentiment_gold        name negativereason_gold  retweet_count  \\\n",
       "0                    NaN     cairdin                 NaN              0   \n",
       "1                    NaN    jnardino                 NaN              0   \n",
       "2                    NaN  yvonnalynn                 NaN              0   \n",
       "3                    NaN    jnardino                 NaN              0   \n",
       "4                    NaN    jnardino                 NaN              0   \n",
       "\n",
       "                                                text tweet_coord  \\\n",
       "0                @VirginAmerica What @dhepburn said.         NaN   \n",
       "1  @VirginAmerica plus you've added commercials t...         NaN   \n",
       "2  @VirginAmerica I didn't today... Must mean I n...         NaN   \n",
       "3  @VirginAmerica it's really aggressive to blast...         NaN   \n",
       "4  @VirginAmerica and it's a really big bad thing...         NaN   \n",
       "\n",
       "               tweet_created tweet_location               user_timezone  \n",
       "0  2015-02-24 11:35:52 -0800            NaN  Eastern Time (US & Canada)  \n",
       "1  2015-02-24 11:15:59 -0800            NaN  Pacific Time (US & Canada)  \n",
       "2  2015-02-24 11:15:48 -0800      Lets Play  Central Time (US & Canada)  \n",
       "3  2015-02-24 11:15:36 -0800            NaN  Pacific Time (US & Canada)  \n",
       "4  2015-02-24 11:14:45 -0800            NaN  Pacific Time (US & Canada)  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "# Load the original dataset and display some of the rows\n",
    "raw_tweets = pd.read_csv(\"data/twitter-airlines-sentiment.csv\")\n",
    "raw_tweets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we are not going to include non-textual information in our futher processing, we can limit the dataset in order to have just two columns of the original file: sentiment and the content of each tweet. With pandas, it may be done quite easily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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     "end_time": "2019-12-03T09:43:19.372424Z",
     "start_time": "2019-12-03T09:43:19.353967Z"
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neutral</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>positive</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>neutral</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>negative</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>negative</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
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      "text/plain": [
       "  sentiment                                               text\n",
       "0   neutral                @VirginAmerica What @dhepburn said.\n",
       "1  positive  @VirginAmerica plus you've added commercials t...\n",
       "2   neutral  @VirginAmerica I didn't today... Must mean I n...\n",
       "3  negative  @VirginAmerica it's really aggressive to blast...\n",
       "4  negative  @VirginAmerica and it's a really big bad thing..."
      ]
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   "source": [
    "# Retrieve only two columns which are going to be used later\n",
    "# and additionally rename them\n",
    "tweets = raw_tweets[[\"airline_sentiment\", \"text\"]]\n",
    "tweets.columns = (\"sentiment\", \"text\", )\n",
    "tweets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to have an intuition about our dataset, let's count how many positive, neutral and negative messages we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "end_time": "2019-12-03T09:43:20.711557Z",
     "start_time": "2019-12-03T09:43:20.694910Z"
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>count</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>negative</td>\n",
       "      <td>9178</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>neutral</td>\n",
       "      <td>3099</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>positive</td>\n",
       "      <td>2363</td>\n",
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      ],
      "text/plain": [
       "  sentiment  count\n",
       "0  negative   9178\n",
       "1   neutral   3099\n",
       "2  positive   2363"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "tweets.groupby(\"sentiment\")\\\n",
    "      .size()\\\n",
    "      .reset_index(name=\"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see, a majority of tweets in the dataset is negative. A model returning the labels randomly, should have an accuracy of 33,33%. On the other hand, a classifier which always returns \"negative\" should achieve an accuracy of 62,69%. And that's the lower limit for the model we are going to create."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data quality\n",
    "For the purposes of Machine Learning we need to make sure the dataset is of a high quality. The first thing to check. when it comes to textual data, is if all the rows are not empty, or if their length is higher than just a few characters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "end_time": "2019-12-03T09:43:22.961012Z",
     "start_time": "2019-12-03T09:43:22.931809Z"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [sentiment, text]\n",
       "Index: []"
      ]
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     "execution_count": 4,
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   "source": [
    "tweets[tweets[\"text\"].str.len() < 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our dataset doesn't contain any empty texts. What's more, there are no texts with the length lower than 10.\n",
    "\n",
    "There are several other things we may find useful to understand the dataset better. Let's consider the distribution of tweets length:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-03T09:43:24.730687Z",
     "start_time": "2019-12-03T09:43:24.402289Z"
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   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from matplotlib import pyplot \n",
    "\n",
    "\n",
    "def display_length_plot(tweets_df):\n",
    "    \"\"\"\n",
    "    Displays a plot of tweets' lengths for given DataFrame.\n",
    "    :param tweets_df: DataFrame\n",
    "    \"\"\"\n",
    "    lengths = tweets_df[\"text\"].str.len()\\\n",
    "                               .reset_index(name=\"length\")\\\n",
    "                               .groupby(\"length\")\\\n",
    "                               .size()\\\n",
    "                               .reset_index(name=\"count\")\n",
    "    lengths.plot(x=\"length\", y=\"count\", kind=\"line\")\n",
    "    \n",
    "display_length_plot(tweets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see how it looks like globally, but it might be also interesting to see if there is any difference between classes. The following lines of code display separate histogram for positive, negative and neutral entries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-03T09:43:26.979716Z",
     "start_time": "2019-12-03T09:43:26.409249Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_length_plot(tweets[tweets[\"sentiment\"] == \"negative\"])\n",
    "display_length_plot(tweets[tweets[\"sentiment\"] == \"neutral\"])\n",
    "display_length_plot(tweets[tweets[\"sentiment\"] == \"positive\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's store the variable for futher usage with a magic function of Jupyter Notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-03T09:43:30.516209Z",
     "start_time": "2019-12-03T09:43:30.485220Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'tweets' (DataFrame)\n"
     ]
    }
   ],
   "source": [
    "%store tweets"
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}