sentiment_analysis.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# r/wallstreetbets Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.11.1.min']\n",
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       "        "
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   "source": [
    "# Setup\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "%matplotlib inline\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected = True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "import plotly.express as px"
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "<bound method NDFrame.head of                                                  title  \\\n",
      "0    Yolo'd my first ever 8k on an FHA 35 mortgage ...   \n",
      "1    Britons use of consumer credit is rising with ...   \n",
      "2                           Norwegian salmon over eggs   \n",
      "3    Calling all dividend investors  Stop making in...   \n",
      "4    Let get fucked This is not how imagined 2022 I...   \n",
      "..                                                 ...   \n",
      "507                              the bill hwang arrest   \n",
      "508                                 Trunk of the night   \n",
      "509  How a rogue VIX spike last week predicted the ...   \n",
      "510  Trojan Condom Maker Cautions About Potential R...   \n",
      "511                                       Coke Pumping   \n",
      "\n",
      "                                                  text sentiment  \n",
      "0                                                  NaN  positive  \n",
      "1    Excuse my retardedness but couldn't this lead ...  negative  \n",
      "2                                                  NaN   neutral  \n",
      "3    Calling all dividend investors   Stop making i...  negative  \n",
      "4                                                  NaN  negative  \n",
      "..                                                 ...       ...  \n",
      "507  I am of the opinion that the arrest of bill hw...   neutral  \n",
      "508  This is a for real question i know mods im not...   neutral  \n",
      "509  gtPt. 1  42022   gt   gtThe rogue VIX spike th...  negative  \n",
      "510  Bloomberg  The maker of Arm amp Hammer baking ...  negative  \n",
      "511  I may not be the smartest ape in the bunch but...  positive  \n",
      "\n",
      "[512 rows x 3 columns]>\n"
     ]
    }
   ],
   "source": [
    "wsb = pd.read_csv(\"./wsbsentiment.csv\", names = ['title', 'text', 'sentiment'], encoding = \"utf-8\", encoding_errors = 'ignore')\n",
    "\n",
    "print(wsb.head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.histogram(wsb, x = \"sentiment\")\n",
    "fig.update_traces(marker_color=\"turquoise\",marker_line_color='rgb(8,48,107)',\n",
    "                  marker_line_width=1.5)\n",
    "fig.update_layout(title_text='Sentiment Score')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes Classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the `punkt` tokenizing library from `nltk`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\kim3\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\kim3\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove neutral sentiment, since it kind of messes things up, then make a new list of lists with just the `title` and `sentiment` (dropping `text`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "wsblist = wsb.drop(columns = ['text'])\n",
    "wsbtext = wsb.dropna(subset = ['text'])\n",
    "wsbtext = wsbtext.drop(columns = ['title'])\n",
    "wsblist = wsblist[wsblist['sentiment'] != 'neutral']\n",
    "wsblist = wsblist.values.tolist()\n",
    "wsbtext = wsbtext[wsbtext['sentiment'] != 'neutral']\n",
    "wsbtext = wsbtext.values.tolist()\n",
    "wsblist = wsblist + wsbtext"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove stopwords and tokenize the words (painfully) (and by hand)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_words = set(word.lower() for post in wsblist for word in word_tokenize(post[0]))\n",
    "stopwords = nltk.corpus.stopwords.words('english')\n",
    "stopwords.append('\\'s')\n",
    "stopwords.append('\\'d')\n",
    "for stopword in stopwords:\n",
    "    all_words.discard(stopword)\n",
    "import pickle\n",
    "words = []\n",
    "try:\n",
    "    with open ('words.pkl', 'rb') as fp:\n",
    "        words = pickle.load(fp)\n",
    "except:\n",
    "    words = [({word: (word in word_tokenize(post[0])) for word in all_words}, post[1]) for post in wsblist]\n",
    "    with open ('words.pkl', 'wb') as fp:\n",
    "        pickle.dump(words, fp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "80/20 split on the training versus testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "640\n"
     ]
    }
   ],
   "source": [
    "print(len(words))\n",
    "train_data = words[:(round(len(words) * 0.8))]\n",
    "test_data = words[(round(len(words) * 0.8)):]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the Bayes classifier on the training data, then determine accuracy based on test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is  0.625\n",
      "Most Informative Features\n",
      "                 bullish = True           positi : negati =      8.2 : 1.0\n",
      "                 showing = True           positi : negati =      5.8 : 1.0\n",
      "                  longer = True           negati : positi =      5.4 : 1.0\n",
      "                      25 = True           positi : negati =      5.1 : 1.0\n",
      "                    best = True           positi : negati =      5.1 : 1.0\n",
      "                   covid = True           positi : negati =      5.1 : 1.0\n",
      "                   looks = True           positi : negati =      5.1 : 1.0\n",
      "                      gt = True           negati : positi =      4.8 : 1.0\n",
      "                  making = True           negati : positi =      4.6 : 1.0\n",
      "                 options = True           positi : negati =      4.5 : 1.0\n",
      "                 bearish = True           positi : negati =      4.3 : 1.0\n",
      "                    come = True           positi : negati =      4.3 : 1.0\n",
      "                    form = True           positi : negati =      4.3 : 1.0\n",
      "                     fun = True           positi : negati =      4.3 : 1.0\n",
      "                    line = True           positi : negati =      4.3 : 1.0\n",
      "                   lower = True           positi : negati =      4.3 : 1.0\n",
      "               positions = True           positi : negati =      4.3 : 1.0\n",
      "                  pretty = True           positi : negati =      4.3 : 1.0\n",
      "                   weeks = True           positi : negati =      4.3 : 1.0\n",
      "                   words = True           positi : negati =      4.3 : 1.0\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "from nltk import classify\n",
    "from nltk import NaiveBayesClassifier\n",
    "classifier = NaiveBayesClassifier.train(train_data)\n",
    "print('Accuracy is ', classify.accuracy(classifier, test_data))\n",
    "print(classifier.show_most_informative_features(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This could probably be better with more data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a custom classifier. Write a custom post in the `custom_wsb` variable, then run the cell to see what the classifier thinks the post's sentiment is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative\n"
     ]
    }
   ],
   "source": [
    "from nltk.tokenize import word_tokenize\n",
    "custom_wsb = \"contracts\"\n",
    "custom_tokens = word_tokenize(custom_wsb)\n",
    "print(classifier.classify(dict([token, True] for token in custom_tokens)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try words like \"bullish\", \"bearish\", \"bull\", \"infinity\", or \"hedge fund\"!"
   ]
  }
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