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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "lawNHLqffR_m"
},
"source": [
"# SCC0633/SCC5908 - Processamento de Linguagem Natural\n",
"> **Docente:** Thiago Alexandre Salgueiro Pardo \\\n",
"> **Estagiário PAE:** Germano Antonio Zani Jorge\n",
"\n",
"\n",
"# Integrantes do Grupo: GPTrouxas\n",
"> André Guarnier De Mitri - 11395579 \\\n",
"> Daniel Carvalho - 10685702 \\\n",
"> Fernando - 11795342 \\\n",
"> Lucas Henrique Sant'Anna - 10748521 \\\n",
"> Magaly L Fujimoto - 4890582"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pV6WGoBln8id"
},
"source": [
"# New Section"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Abordagem Estatístico\n",
"A arquitetura da solução estatística/neural envolve duas abordagens que\n",
"serão descritas neste documento. A primeira abordagem envolve utilizar\n",
"TF-IDF e Naive Bayes. E a segunda abordagem irá utilizar Word2Vec e um\n",
"modelo transformers pré-treinado da família BERT, realizando finetuning do\n",
"modelo.\n",
"\n",
"Na primeira abordagem, utilizaremos o TF-IDF, que leva em consideração a\n",
"frequência de ocorrência dos termos em um corpus e gera uma sequência de\n",
"vetores que serão fornecidos ao Naive Bayes para classificação da review como\n",
"positiva ou negativa.\n",
"\n",
"\n",
"Na segunda abordagem, utilizaremos o Word2Vec para vetorizar as reviews.\n",
"Após dividir em treino e teste, faremos o fine tuning de um modelo do tipo BERT\n",
"para o nosso problema e dataset específico. Com o BERT adaptado, faremos a\n",
"classificação de nossos textos, medindo o seu desempenho com F1 score e\n",
"acurácia.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vfP54aryxZBg"
},
"source": [
"\n",
"## # Etapas da Abordagem Estatística\n",
"\n",
"1. **Bibliotecas**: Importamos as bibliotecas necessárias, considerando pandas para manipulação de dados, train_test_split para dividir o conjunto de dados em conjuntos de treinamento e teste, TfidfVectorizer para vetorização de texto usando TF-IDF, MultinomialNB para implementar o classificador Naive Bayes Multinomial e algumas métricas de avaliação.\n",
"\n",
"2. **Conjunto de dados**: Carregar o conjunto de dados e armazená-lo em um dataframe usando pandas.\n",
"\n",
"3. **Dividir o conjunto de dados**: Usamos `train_test_split` para dividir o DataFrame em conjuntos de treinamento e teste.\n",
"\n",
"4. **TF-IDF**: Usamos `TfidfVectorizer` para converter as revisões de texto em vetores numéricos usando a técnica TF-IDF. Em seguida, ajustamos e transformamos tanto o conjunto de treinamento quanto o conjunto de teste.\n",
"\n",
"5. **Naive Bayes**: Treinamos um classificador Naive Bayes Multinomial e usamos o modelo treinado para prever os sentimentos no conjunto de teste usando `predict`.\n",
"\n",
"6. **Avaliação e Resultados**: Salvamos os resultados em um novo dataframe `results_df` contendo as revisões do conjunto de teste, os sentimentos originais e os sentimentos previstos pelo modelo. Além disso, avaliamos o modelo verificando algumas métricas e a matriz de confusão.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TbLraa4UhWDJ"
},
"source": [
"\n",
"## # Baixando, Carregando os dados e Pré Processamento\n",
"\n",
"1. Transformar todos os textos em lowercase \\\\\n",
"2. Remoção de caracteres especiais \\\\\n",
"3. Remoção de stop words \\\\\n",
"4. Lematização (Lemmatization) \\\\\n",
"5. Tokenização \\\\"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "bIWmIe0qfTbE"
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "Wf0n2yPdAn4C",
"outputId": "37eb3c4d-40c1-41a0-9b1a-d93ed6e272f3"
},
"outputs": [
{
"data": {
"text/html": [
"<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>review</th>\n",
" <th>sentiment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>One of the other reviewers has mentioned that ...</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A wonderful little production. <br /><br />The...</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>I thought this was a wonderful way to spend ti...</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Basically there's a family where a little boy ...</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" review sentiment\n",
"0 One of the other reviewers has mentioned that ... positive\n",
"1 A wonderful little production. <br /><br />The... positive\n",
"2 I thought this was a wonderful way to spend ti... positive\n",
"3 Basically there's a family where a little boy ... negative\n",
"4 Petter Mattei's \"Love in the Time of Money\" is... positive"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db = pd.read_csv('../data/imdb_reviews.csv')\n",
"db.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6PlfPScGMF1_",
"outputId": "2a0bd4a1-e22a-429d-82a4-5984eeab7b9d"
},
"outputs": [
{
"data": {
"text/plain": [
"sentiment\n",
"positive 25000\n",
"negative 25000\n",
"Name: count, dtype: int64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db['sentiment'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Kev0EaSmMa4N",
"outputId": "eab73a61-ba36-4d72-e4f2-82236f9f2880"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Quantidade de valores faltantes para cada variável do dataset:\n",
"review 0\n",
"sentiment 0\n",
"dtype: int64\n"
]
}
],
"source": [
"valores_ausentes = db.isnull().sum(axis=0)\n",
"print('Quantidade de valores faltantes para cada variável do dataset:')\n",
"print(valores_ausentes)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 276
},
"id": "1AI3rN0KMuUq",
"outputId": "7ea5c91b-362e-49eb-82a7-6e8535f0e591"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to\n",
"[nltk_data] C:\\Users\\andre\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n",
"[nltk_data] Downloading package wordnet to\n",
"[nltk_data] C:\\Users\\andre\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Package wordnet is already up-to-date!\n"
]
},
{
"data": {
"text/html": [
"<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>review</th>\n",
" <th>sentiment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>one reviewer mentioned watching 1 oz episode h...</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>wonderful little production filming technique ...</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>thought wonderful way spend time hot summer we...</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>basically family little boy jake think zombie ...</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>petter mattei love time money visually stunnin...</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" review sentiment\n",
"0 one reviewer mentioned watching 1 oz episode h... positive\n",
"1 wonderful little production filming technique ... positive\n",
"2 thought wonderful way spend time hot summer we... positive\n",
"3 basically family little boy jake think zombie ... negative\n",
"4 petter mattei love time money visually stunnin... positive"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import re\n",
"import nltk\n",
"from nltk.corpus import stopwords\n",
"from nltk.stem import PorterStemmer\n",
"from nltk.stem import WordNetLemmatizer\n",
"\n",
"def lowercase_text(text):\n",
" return text.lower()\n",
"\n",
"def remove_html(text):\n",
" return re.sub(r'<[^<]+?>', '', text)\n",
"\n",
"def remove_url(text):\n",
" return re.sub(r'http[s]?://\\S+|www\\.\\S+', '', text)\n",
"\n",
"def remove_punctuations(text):\n",
" tokens_list = '!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'\n",
" for char in text:\n",
" if char in tokens_list:\n",
" text = text.replace(char, ' ')\n",
"\n",
" return text\n",
"\n",
"def remove_emojis(text):\n",
" emojis = re.compile(\"[\"\n",
" u\"\\U0001F600-\\U0001F64F\"\n",
" u\"\\U0001F300-\\U0001F5FF\"\n",
" u\"\\U0001F680-\\U0001F6FF\"\n",
" u\"\\U0001F1E0-\\U0001F1FF\"\n",
" u\"\\U00002500-\\U00002BEF\"\n",
" u\"\\U00002702-\\U000027B0\"\n",
" u\"\\U00002702-\\U000027B0\"\n",
" u\"\\U000024C2-\\U0001F251\"\n",
" u\"\\U0001f926-\\U0001f937\"\n",
" u\"\\U00010000-\\U0010ffff\"\n",
" u\"\\u2640-\\u2642\"\n",
" u\"\\u2600-\\u2B55\"\n",
" u\"\\u200d\"\n",
" u\"\\u23cf\"\n",
" u\"\\u23e9\"\n",
" u\"\\u231a\"\n",
" u\"\\ufe0f\"\n",
" u\"\\u3030\"\n",
" \"]+\", re.UNICODE)\n",
"\n",
" text = re.sub(emojis, '', text)\n",
" return text\n",
"\n",
"def remove_stop_words(text):\n",
" stop_words = stopwords.words('english')\n",
" new_text = ''\n",
" for word in text.split():\n",
" if word not in stop_words:\n",
" new_text += ''.join(f'{word} ')\n",
"\n",
" return new_text.strip()\n",
"\n",
"def lem_words(text):\n",
" lemma = WordNetLemmatizer()\n",
" new_text = ''\n",
" for word in text.split():\n",
" new_text += ''.join(f'{lemma.lemmatize(word)} ')\n",
"\n",
" return new_text\n",
"\n",
"def preprocess_text(text):\n",
" text = lowercase_text(text)\n",
" text = remove_html(text)\n",
" text = remove_url(text)\n",
" text = remove_punctuations(text)\n",
" text = remove_emojis(text)\n",
" text = remove_stop_words(text)\n",
" text = lem_words(text)\n",
"\n",
" return text\n",
"\n",
"nltk.download('stopwords')\n",
"nltk.download('wordnet')\n",
"db['review'] = db['review'].apply(preprocess_text)\n",
"db.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QgufZpgHnPa4"
},
"source": [
"# **Conjunto de Treino e teste**"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "s0lJ6Q0tnPka"
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"X= db['review']\n",
"y= db['sentiment']\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2, random_state= 12)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nz4erCEJuD4-",
"outputId": "88d57536-66e7-4d9b-e016-bf40183d4c45"
},
"outputs": [
{
"data": {
"text/plain": [
"35235 disagree people saying lousy horror film good ...\n",
"36936 husband wife doctor team carole nile nelson mo...\n",
"46486 like cast pretty much however story sort unfol...\n",
"27160 movie awful bad bear expend anything word avoi...\n",
"19490 purchased blood castle dvd ebay buck knowing s...\n",
" ... \n",
"36482 strange thing see film scene work rather weakl...\n",
"40177 saw cheap dvd release title entity force since...\n",
"19709 one peculiar oft used romance movie plot one s...\n",
"38555 nothing positive say meandering nonsense huffi...\n",
"14155 low moment life bewildered depressed sitting r...\n",
"Name: review, Length: 40000, dtype: object"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6LX-6e-QlioJ"
},
"source": [
"# **TD-IDF e Naive Bayes**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "gscB9-obNusA"
},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix,classification_report\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.preprocessing import StandardScaler as encoder\n",
"from sklearn.metrics import (\n",
" accuracy_score,\n",
" confusion_matrix,\n",
" ConfusionMatrixDisplay,\n",
" f1_score,\n",
")\n",
"\n",
"\n",
"tfidf = TfidfVectorizer()\n",
"tfidf_train = tfidf.fit_transform(X_train)\n",
"tfidf_test = tfidf.transform(X_test)\n",
"\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"\n",
"naive_bayes = MultinomialNB()\n",
"\n",
"naive_bayes.fit(tfidf_train, y_train)\n",
"y_pred = naive_bayes.predict(tfidf_test)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "RfJ7AHMZvAb8",
"outputId": "685701e1-b1e8-47fb-9dc5-1bc04dd3894b"
},
"outputs": [
{
"data": {
"text/html": [
"<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>review</th>\n",
" <th>original sentiment</th>\n",
" <th>predicted sentiment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>34622</th>\n",
" <td>hard tell noonan marshall trying ape abbott co...</td>\n",
" <td>negative</td>\n",
" <td>negative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1163</th>\n",
" <td>well start one reviewer said know real treat s...</td>\n",
" <td>positive</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7637</th>\n",
" <td>wife kid opinion absolute abc classic seen eve...</td>\n",
" <td>positive</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7045</th>\n",
" <td>surprise basic copycat comedy classic nutty pr...</td>\n",
" <td>positive</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43847</th>\n",
" <td>josef von sternberg directs magnificent silent...</td>\n",
" <td>positive</td>\n",
" <td>positive</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" review original sentiment \\\n",
"34622 hard tell noonan marshall trying ape abbott co... negative \n",
"1163 well start one reviewer said know real treat s... positive \n",
"7637 wife kid opinion absolute abc classic seen eve... positive \n",
"7045 surprise basic copycat comedy classic nutty pr... positive \n",
"43847 josef von sternberg directs magnificent silent... positive \n",
"\n",
" predicted sentiment \n",
"34622 negative \n",
"1163 positive \n",
"7637 positive \n",
"7045 positive \n",
"43847 positive "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Criando DataFrame com resultados\n",
"results_df = pd.DataFrame({'review': X_test, 'original sentiment': y_test, 'predicted sentiment': y_pred})\n",
"results_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8Xq2ABXYtsjk"
},
"source": [
"## Avaliação"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "3lXqDNhSrhsZ"
},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix, classification_report\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"def plot_confusion_matrix(y_true, y_pred, labels, model_name):\n",
" cm = confusion_matrix(y_true, y_pred, labels=labels)\n",
" plt.figure(figsize=(8, 6))\n",
" sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
" plt.xlabel('Predicted Labels')\n",
" plt.ylabel('True Labels')\n",
" plt.title(f'Confusion Matrix {model_name}')\n",
" plt.show()\n",
"\n",
"# Função para calcular e imprimir as métricas de avaliação\n",
"def print_evaluation_metrics(y_true, y_pred, model_name):\n",
" print(f\"Classification Report {model_name}:\")\n",
" print(classification_report(y_true, y_pred))\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 564
},
"id": "ybfb_GKDuqmb",
"outputId": "3e4c3a98-8962-4ce8-9856-2252f769a1b8"
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_confusion_matrix(y_test, y_pred, ['positive', 'negative'], 'NB')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2580FJCGs_oQ",
"outputId": "118f79e2-6b57-4cc0-a631-c2ef8a7e317e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Classification Report NB:\n",
" precision recall f1-score support\n",
"\n",
" negative 0.86 0.87 0.86 5017\n",
" positive 0.87 0.86 0.86 4983\n",
"\n",
" accuracy 0.86 10000\n",
" macro avg 0.86 0.86 0.86 10000\n",
"weighted avg 0.86 0.86 0.86 10000\n",
"\n"
]
}
],
"source": [
"# Imprimir as métricas de avaliação\n",
"print_evaluation_metrics(y_test, y_pred, 'NB')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "x0JBy6nXvdjC"
},
"source": [
"# Conclusão\n",
"\n",
"É possível verificar no relatório de classificação que precisão e recall estão variando entre 86 a 87%. A métrica **F1-Score** combina precisão e recall, possui valor de aproximadamente 86%, o que indica um bom equilíbrio entre precisão e recall. A **Acurácia (accuracy)** geral do modelo é de 86%, o que significa que ele classificou corretamente aproximadamente 86% de todos os exemplos no conjunto de teste.\n",
"\n",
"O modelo Naive Bayes com vetorização TF-IDF conseguiu alcançar uma precisão, recall e F1-Score bastante equilibrados para ambas as classes, com uma acurácia geral de 86%. Podemos afirmar que o modelo é capaz de fazer previsões precisas em relação ao sentimento das revisões. Assim, podemos afirmar que o modelo estatístico possui um desempenho consideravelmente superior em relação à abordagem simbólica.\n"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"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": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|