Upload files to "klasifikasi teks FNN"
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klasifikasi teks FNN/Untitled0.ipynb
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klasifikasi teks FNN/Untitled0.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"id": "PiGdhtYtor9F"
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"from sklearn.svm import LinearSVC\n",
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"from sklearn.naive_bayes import MultinomialNB\n",
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"from sklearn.metrics import classification_report, accuracy_score\n",
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"\n",
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"# Sample dataset (replace with your actual dataset)\n",
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"df = pd.DataFrame({\n",
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" 'text': [\n",
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" 'This is a positive review.',\n",
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" 'Negative experience, very bad.',\n",
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" 'Neutral product, neither good nor bad.',\n",
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" 'I absolutely love this!',\n",
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" 'Terrible service and slow delivery.',\n",
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" 'It was okay, nothing special.'\n",
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" ],\n",
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" 'label': ['positive', 'negative', 'neutral', 'positive', 'negative', 'neutral']\n",
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"})\n",
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"# If you have a CSV file, uncomment the line below and comment out the sample DataFrame above:\n",
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"# df = pd.read_csv(\"dataset.csv\")\n",
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"# Make sure your CSV has 'text' and 'label' columns.\n",
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"\n",
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"# PREPROCESSING\n",
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"X = df['text']\n",
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"y = df['label']\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(\n",
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" X, y, test_size=0.2, random_state=42\n",
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")\n",
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"\n",
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"vectorizer = TfidfVectorizer(max_features=5000)\n",
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"X_train_tfidf = vectorizer.fit_transform(X_train)\n",
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"X_test_tfidf = vectorizer.transform(X_test)\n"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"svm_model = LinearSVC()\n",
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"svm_model.fit(X_train_tfidf, y_train)\n",
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"\n",
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"svm_pred = svm_model.predict(X_test_tfidf)\n",
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"\n",
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"print(\"=== SVM Accuracy ===\")\n",
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"print(accuracy_score(y_test, svm_pred))\n",
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"print(\"\\n=== Classification Report SVM ===\")\n",
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"print(classification_report(y_test, svm_pred))\n"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "mUdBJD5gpELB",
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"outputId": "ec430145-2b3d-4ea9-e121-8abdc90f5119"
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},
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"execution_count": 3,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"=== SVM Accuracy ===\n",
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"0.5\n",
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"\n",
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"=== Classification Report SVM ===\n",
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" precision recall f1-score support\n",
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"\n",
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" negative 0.00 0.00 0.00 1\n",
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" neutral 0.00 0.00 0.00 0\n",
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" positive 1.00 1.00 1.00 1\n",
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"\n",
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" accuracy 0.50 2\n",
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" macro avg 0.33 0.33 0.33 2\n",
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"weighted avg 0.50 0.50 0.50 2\n",
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"\n"
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]
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},
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
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" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
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"/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
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" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
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"/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
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" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
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"/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
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" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
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"/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
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" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
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"/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
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" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"nb_model = MultinomialNB()\n",
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"nb_model.fit(X_train_tfidf, y_train)\n",
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"\n",
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"nb_pred = nb_model.predict(X_test_tfidf)\n",
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"\n",
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"print(\"=== Naive Bayes Accuracy ===\")\n",
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"print(accuracy_score(y_test, nb_pred))\n",
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"print(\"\\n=== Classification Report Naive Bayes ===\")\n",
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"print(classification_report(y_test, nb_pred))\n",
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"\n"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "JeIVhQVopJcv",
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"outputId": "22e504c9-f279-49f7-b190-bd04b1986ec4"
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},
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"execution_count": 4,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"=== Naive Bayes Accuracy ===\n",
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"0.0\n",
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"\n",
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"=== Classification Report Naive Bayes ===\n",
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" precision recall f1-score support\n",
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"\n",
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" negative 0.00 0.00 0.00 1.0\n",
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" neutral 0.00 0.00 0.00 0.0\n",
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" positive 0.00 0.00 0.00 1.0\n",
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"\n",
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" accuracy 0.00 2.0\n",
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" macro avg 0.00 0.00 0.00 2.0\n",
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"weighted avg 0.00 0.00 0.00 2.0\n",
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"\n"
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]
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},
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
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" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
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"/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
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" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
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"/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
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" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
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"/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
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" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
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"/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
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" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
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"/usr/local/lib/python3.12/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
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" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"sample_text = [\"barang buruk, saya kecewa\"]\n",
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"\n",
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"sample_vec = vectorizer.transform(sample_text)\n",
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"\n",
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"print(\"SVM Prediction:\", svm_model.predict(sample_vec)[0])\n",
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"print(\"Naive Bayes Prediction:\", nb_model.predict(sample_vec)[0])\n"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "Axyz7fN9pVf0",
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"outputId": "2c1e2e67-8c0c-48c1-df58-69ad52ca9675"
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},
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"execution_count": 6,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"SVM Prediction: neutral\n",
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"Naive Bayes Prediction: neutral\n"
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]
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}
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]
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}
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]
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}
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