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Add error handling, Change in clusters #80

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May 19, 2024
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Add error handling, Change in clusters #80

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29 changes: 29 additions & 0 deletions .ipynb_checkpoints/clustering movie review-checkpoint.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,8 @@
"from sklearn.cluster import KMeans\n",
"from sklearn.metrics import silhouette_score\n",
"from nltk.sentiment import SentimentIntensityAnalyzer\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.decomposition import PCA\n",
"\n",
"# Load NLTK's sentiment analyzer\n",
"sid = SentimentIntensityAnalyzer()\n",
Expand All @@ -36,11 +38,38 @@
"tfidf_vectorizer = TfidfVectorizer(max_features=1000) # You can adjust max_features as needed\n",
"tfidf_matrix = tfidf_vectorizer.fit_transform(data['clean_text'])\n",
"\n",
"# Optimal number of clusters\n",
"inertia = []\n",
"for k in range(2, 11):\n",
" kmeans = KMeans(n_clusters=k, random_state=42)\n",
" kmeans.fit(tfidf_matrix)\n",
" inertia.append(kmeans.inertia_)\n",
"\n",
"plt.plot(range(2, 11), inertia, marker='o')\n",
"plt.xlabel('Number of Clusters')\n",
"plt.ylabel('Inertia')\n",
"plt.title('Elbow Method')\n",
"plt.show()\n",
"\n",
"# Clustering with K-means\n",
"k = 5 # Number of clusters (you can adjust this)\n",
"kmeans = KMeans(n_clusters=k, random_state=42)\n",
"kmeans.fit(tfidf_matrix)\n",
"\n",
"# Dimensionality reduction for visualization\n",
"pca = PCA(n_components=2)\n",
"tfidf_matrix_2d = pca.fit_transform(tfidf_matrix.toarray())\n",
"\n",
"# Visualize clusters\n",
"plt.figure(figsize=(10, 6))\n",
"for i in range(k):\n",
" plt.scatter(tfidf_matrix_2d[kmeans.labels_ == i, 0], tfidf_matrix_2d[kmeans.labels_ == i, 1], label=f'Cluster {i+1}')\n",
"plt.title('Clusters Visualization')\n",
"plt.xlabel('PCA Component 1')\n",
"plt.ylabel('PCA Component 2')\n",
"plt.legend()\n",
"plt.show()\n",
"\n",
"# Assign cluster labels to each review\n",
"data['cluster_label'] = kmeans.labels_\n",
"\n",
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