diff --git "a/notebook.ipynb" "b/notebook.ipynb" --- "a/notebook.ipynb" +++ "b/notebook.ipynb" @@ -7,58 +7,278 @@ "# Sentiment Analysis" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "from __future__ import annotations\n", "\n", + "from typing import TYPE_CHECKING\n", + "\n", + "if TYPE_CHECKING:\n", + " from sklearn.base import BaseEstimator\n", + "\n", "import re\n", + "import warnings\n", "from functools import cache\n", "\n", "import matplotlib.pyplot as plt\n", + "import nltk\n", + "import numpy as np\n", "import pandas as pd\n", - "import seaborn as sns" + "import seaborn as sns\n", + "from joblib import Memory\n", + "from nltk.corpus import stopwords\n", + "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.model_selection import RandomizedSearchCV, train_test_split\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.svm import SVC\n", + "\n", + "from app.constants import CACHE_DIR, SENTIMENT140_PATH\n", + "from app.model import TextCleaner, TextLemmatizer" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "SEED = 42\n", + "MAX_FEATURES = 20000" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package wordnet to /home/tymec/nltk_data...\n", + "[nltk_data] Package wordnet is already up-to-date!\n", + "[nltk_data] Downloading package stopwords to /home/tymec/nltk_data...\n", + "[nltk_data] Unzipping corpora/stopwords.zip.\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nltk.download(\"wordnet\")\n", + "nltk.download(\"stopwords\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Load the data" + "## Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the data" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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targetiddateflagusertextsentiment
001467810369Mon Apr 06 22:19:45 PDT 2009NO_QUERY_TheSpecialOne_@switchfoot http://twitpic.com/2y1zl - Awww, t...negative
101467810672Mon Apr 06 22:19:49 PDT 2009NO_QUERYscotthamiltonis upset that he can't update his Facebook by ...negative
201467810917Mon Apr 06 22:19:53 PDT 2009NO_QUERYmattycus@Kenichan I dived many times for the ball. Man...negative
301467811184Mon Apr 06 22:19:57 PDT 2009NO_QUERYElleCTFmy whole body feels itchy and like its on firenegative
401467811193Mon Apr 06 22:19:57 PDT 2009NO_QUERYKaroli@nationwideclass no, it's not behaving at all....negative
\n", + "
" + ], + "text/plain": [ + " target id date flag \\\n", + "0 0 1467810369 Mon Apr 06 22:19:45 PDT 2009 NO_QUERY \n", + "1 0 1467810672 Mon Apr 06 22:19:49 PDT 2009 NO_QUERY \n", + "2 0 1467810917 Mon Apr 06 22:19:53 PDT 2009 NO_QUERY \n", + "3 0 1467811184 Mon Apr 06 22:19:57 PDT 2009 NO_QUERY \n", + "4 0 1467811193 Mon Apr 06 22:19:57 PDT 2009 NO_QUERY \n", + "\n", + " user text \\\n", + "0 _TheSpecialOne_ @switchfoot http://twitpic.com/2y1zl - Awww, t... \n", + "1 scotthamilton is upset that he can't update his Facebook by ... \n", + "2 mattycus @Kenichan I dived many times for the ball. Man... \n", + "3 ElleCTF my whole body feels itchy and like its on fire \n", + "4 Karoli @nationwideclass no, it's not behaving at all.... \n", + "\n", + " sentiment \n", + "0 negative \n", + "1 negative \n", + "2 negative \n", + "3 negative \n", + "4 negative " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "data: pd.DataFrame = None # TODO: load dataset\n", - "stopwords: set[str] = None # TODO: load stopwords" + "# Load the data\n", + "data = pd.read_csv(\n", + " SENTIMENT140_PATH,\n", + " encoding=\"ISO-8859-1\",\n", + " names=[\n", + " \"target\", # 0 = negative, 2 = neutral, 4 = positive\n", + " \"id\", # The id of the tweet\n", + " \"date\", # The date of the tweet\n", + " \"flag\", # The query, NO_QUERY if not present\n", + " \"user\", # The user that tweeted\n", + " \"text\", # The text of the tweet\n", + " ],\n", + ")\n", + "\n", + "# Ignore rows with neutral sentiment\n", + "data = data[data[\"target\"] != 2]\n", + "\n", + "# Map the sentiment values\n", + "data[\"sentiment\"] = data[\"target\"].map({0: \"negative\", 4: \"positive\"})\n", + "\n", + "# Show the first few rows\n", + "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Explore the data" + "### Load the stopwords" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], + "source": [ + "stopwords_en = stopwords.words(\"english\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explore the data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the distribution\n", "_, ax = plt.subplots(figsize=(6, 4))\n", @@ -71,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -82,9 +302,78 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
wordcount
0i750749
1to564469
2the520036
3a377506
4my314024
\n", + "
" + ], + "text/plain": [ + " word count\n", + "0 i 750749\n", + "1 to 564469\n", + "2 the 520036\n", + "3 a 377506\n", + "4 my 314024" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Extract words and count them\n", "words = data[\"text\"].apply(extract_words).explode()\n", @@ -95,9 +384,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot the most common words\n", "_, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n", @@ -109,7 +409,7 @@ "\n", "ax2.set_title(\"Most common words (excluding stopwords)\")\n", "sns.barplot(\n", - " data=word_counts[~word_counts[\"word\"].isin(stopwords)].head(10),\n", + " data=word_counts[~word_counts[\"word\"].isin(stopwords_en)].head(10),\n", " x=\"count\",\n", " y=\"word\",\n", " ax=ax2,\n", @@ -125,14 +425,689 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Find best classifier" + "### Split the data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Set the features and target\n", + "X, y = data[\"text\"].tolist(), data[\"sentiment\"].tolist()\n", + "\n", + "# Split the data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=SEED)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Find best hyperparameters" + "## Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Preprocess and tokenize the data" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the preprocessing pipeline\n", + "preprocess_pipeline = Pipeline(\n", + " [\n", + " # Text preprocessing\n", + " (\"clean\", TextCleaner()),\n", + " (\"lemma\", TextLemmatizer()),\n", + " # Preprocess (NOTE: Can be replaced with TfidfVectorizer, but left for clarity)\n", + " (\"vectorize\", CountVectorizer(stop_words=stopwords_en, ngram_range=(1, 2), max_features=MAX_FEATURES)),\n", + " (\"tfidf\", TfidfTransformer()),\n", + " ],\n", + " memory=Memory(CACHE_DIR, verbose=0),\n", + " verbose=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Pipeline] ......... (step 3 of 4) Processing vectorize, total= 41.9s\n", + "[Pipeline] ............. (step 4 of 4) Processing tfidf, total= 0.0s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/tymec/university/projekt-psi/.venv/lib/python3.12/site-packages/joblib/memory.py:577: UserWarning: Persisting input arguments took 0.55s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function.\n", + " return self._cached_call(args, kwargs, shelving=False)[0]\n" + ] + }, + { + "data": { + "text/html": [ + "
Pipeline(memory=Memory(location=.cache),\n",
+       "         steps=[('clean', TextCleaner()), ('lemma', TextLemmatizer()),\n",
+       "                ('vectorize',\n",
+       "                 CountVectorizer(max_features=20000, ngram_range=(1, 2),\n",
+       "                                 stop_words=['i', 'me', 'my', 'myself', 'we',\n",
+       "                                             'our', 'ours', 'ourselves', 'you',\n",
+       "                                             "you're", "you've", "you'll",\n",
+       "                                             "you'd", 'your', 'yours',\n",
+       "                                             'yourself', 'yourselves', 'he',\n",
+       "                                             'him', 'his', 'himself', 'she',\n",
+       "                                             "she's", 'her', 'hers', 'herself',\n",
+       "                                             'it', "it's", 'its', 'itself', ...])),\n",
+       "                ('tfidf', TfidfTransformer())],\n",
+       "         verbose=True)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(memory=Memory(location=.cache),\n", + " steps=[('clean', TextCleaner()), ('lemma', TextLemmatizer()),\n", + " ('vectorize',\n", + " CountVectorizer(max_features=20000, ngram_range=(1, 2),\n", + " stop_words=['i', 'me', 'my', 'myself', 'we',\n", + " 'our', 'ours', 'ourselves', 'you',\n", + " \"you're\", \"you've\", \"you'll\",\n", + " \"you'd\", 'your', 'yours',\n", + " 'yourself', 'yourselves', 'he',\n", + " 'him', 'his', 'himself', 'she',\n", + " \"she's\", 'her', 'hers', 'herself',\n", + " 'it', \"it's\", 'its', 'itself', ...])),\n", + " ('tfidf', TfidfTransformer())],\n", + " verbose=True)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fit the pipeline\n", + "with warnings.catch_warnings():\n", + " warnings.simplefilter(\"ignore\")\n", + " preprocess_pipeline.fit(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Transform the data\n", + "X_train_preprocessed = preprocess_pipeline.transform(X_train)\n", + "X_test_preprocessed = preprocess_pipeline.transform(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pick the classifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_model(clf: BaseEstimator) -> None:\n", + " # Calculate the accuracy\n", + " accuracy = clf.score(X_test_preprocessed, y_test)\n", + "\n", + " # Calculate the confusion matrix\n", + " y_pred = clf.predict(X_test_preprocessed)\n", + " cm = confusion_matrix(y_test, y_pred)\n", + "\n", + " # Plot the confusion matrix\n", + " categories = [\"Negative\", \"Positive\"]\n", + " group_names = [\"True Neg\", \"False Pos\", \"False Neg\", \"True Pos\"]\n", + " group_percentages = [f\"{value:.2%}\" for value in cm.flatten() / cm.sum()]\n", + "\n", + " labels = [f\"{v1}\\n{v2}\" for v1, v2 in zip(group_names, group_percentages)]\n", + " labels = np.asarray(labels).reshape(2, 2)\n", + "\n", + " _, ax = plt.subplots(figsize=(8, 6))\n", + " ax.grid(False)\n", + " ax.set_title(f\"Accuracy: {accuracy:.2%}\")\n", + " sns.heatmap(\n", + " cm,\n", + " xticklabels=categories,\n", + " yticklabels=categories,\n", + " annot=labels,\n", + " square=True,\n", + " cbar=False,\n", + " cmap=\"viridis\",\n", + " linewidths=0.5,\n", + " fmt=\"\",\n", + " ax=ax,\n", + " )\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def random_search(clf: BaseEstimator, param_distributions: dict) -> tuple[BaseEstimator, dict]:\n", + " # Create the search\n", + " search = RandomizedSearchCV(\n", + " clf,\n", + " param_distributions,\n", + " n_iter=10,\n", + " scoring=\"accuracy\",\n", + " n_jobs=-1,\n", + " cv=3,\n", + " random_state=SEED,\n", + " verbose=1,\n", + " )\n", + "\n", + " # Fit the search\n", + " search.fit(X_train_preprocessed, y_train)\n", + "\n", + " # Print the best parameters\n", + " print(f\"Best parameters: {search.best_params_}\")\n", + "\n", + " return search.best_estimator_, search.best_params_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Logistic Regression\n", + "lr_clf = LogisticRegression(max_iter=1000, random_state=SEED)\n", + "\n", + "# Find optimal hyperparameters\n", + "best_lr_clf, lr_params = random_search(\n", + " lr_clf,\n", + " {\n", + " \"C\": np.logspace(-4, 4, 20),\n", + " \"solver\": [\"liblinear\", \"saga\"],\n", + " \"penalty\": [\"l1\", \"l2\"],\n", + " },\n", + ")\n", + "\n", + "# Evaluate the model\n", + "evaluate_model(best_lr_clf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# SVM\n", + "svm_clf = SVC(random_state=SEED)\n", + "\n", + "# Find optimal hyperparameters\n", + "best_svm_clf, svm_params = random_search(\n", + " svm_clf,\n", + " {\n", + " \"C\": np.logspace(-4, 4, 20),\n", + " \"kernel\": [\"linear\", \"poly\", \"rbf\", \"sigmoid\"],\n", + " \"degree\": [2, 3, 4],\n", + " },\n", + ")\n", + "\n", + "# Evaluate the model\n", + "evaluate_model(best_svm_clf)" ] } ], @@ -143,7 +1118,15 @@ "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": "3.12.3" } },