NMF.ipynb

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   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import NMF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "# Load the Amazon Alexa Reviews dataset\n",
    "df = pd.read_csv('amazon_alexa.csv', encoding='ISO-8859-1')"
   ],
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     "text": [
      "C:\\Users\\Nisaharan.Gen\\anaconda3\\lib\\site-packages\\sklearn\\decomposition\\_nmf.py:289: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n",
      "  warnings.warn(\n",
      "C:\\Users\\Nisaharan.Gen\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
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     "text": [
      "Topic 1:\n",
      "['love', 'absolutely', 'kids', 'fire', 'wonderful', 'new', 'spot', 'our', 'having', 'much']\n",
      "\n",
      "\n",
      "Topic 2:\n",
      "['great', 'addition', 'device', 'purchase', 'buy', 'speaker', 'sound', 'has', 'looks', 'small']\n",
      "\n",
      "\n",
      "Topic 3:\n",
      "['easy', 'setup', 'install', 'smart', 'connect', 'how', 'super', 'its', 'what', 'enjoying']\n",
      "\n",
      "\n",
      "Topic 4:\n",
      "['works', 'well', 'perfect', 'better', 'excellent', 'perfectly', 'other', 'sounds', 'home', 'than']\n",
      "\n",
      "\n",
      "Topic 5:\n",
      "['music', 'all', 'one', 'play', 'more', 'our', 'do', 'what', 'bought', 'am']\n",
      "\n",
      "\n",
      "Topic 6:\n",
      "['like', 'new', 'other', 'do', 'about', 'far', 'almost', 'how', 'little', 'any']\n",
      "\n",
      "\n",
      "Topic 7:\n",
      "['sound', 'quality', 'better', 'than', 'fun', 'much', 'has', 'nice', 'impressed', 'speaker']\n",
      "\n",
      "\n",
      "Topic 8:\n",
      "['good', 'speaker', 'sounds', 'pretty', 'far', 'new', 'device', 'quality', 'small', 'well']\n",
      "\n",
      "\n",
      "Topic 9:\n",
      "['product', 'nice', 'amazing', 'excellent', 'fast', 'awesome', 'music', 'satisfied', 'enjoyment', 'understands']\n",
      "\n",
      "\n",
      "Topic 10:\n",
      "['everything', 'does', 'about', 'expected', 'what', 'perfect', 'loved', 'work', 'learning', 'almost']\n",
      "\n",
      "\n",
      "                                       verified_reviews  dominant_topic  \\\n",
      "0                                         Love my Echo!               0   \n",
      "1                                             Loved it!               9   \n",
      "2     Sometimes while playing a game, you can answer...               5   \n",
      "3     I have had a lot of fun with this thing. My 4 ...               6   \n",
      "4                                                 Music               4   \n",
      "...                                                 ...             ...   \n",
      "3145  Perfect for kids, adults and everyone in betwe...               9   \n",
      "3146  Listening to music, searching locations, check...               4   \n",
      "3147  I do love these things, i have them running my...               4   \n",
      "3148  Only complaint I have is that the sound qualit...               6   \n",
      "3149                                               Good               7   \n",
      "\n",
      "     dominant_topic_name  \n",
      "0                   love  \n",
      "1             everything  \n",
      "2                   like  \n",
      "3                  sound  \n",
      "4                  music  \n",
      "...                  ...  \n",
      "3145          everything  \n",
      "3146               music  \n",
      "3147               music  \n",
      "3148               sound  \n",
      "3149                good  \n",
      "\n",
      "[3150 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# Preprocess the text data by tokenizing and removing stop words, punctuation, and numbers\n",
    "stop_words = set(['a', 'an', 'and', 'the', 'in', 'is', 'it', 'of', 'on', 'with', 'for', 'this', 'by', 'from', 'at', 'to', 'be', 'as', 'but', 'not', 'that', 'or', 'if', 'you', 'are', 'your', 'set', 'here', 'i', 'we', 'my', 'have', 'so', 'she', 'can', 'was', 'echo', 'up', 'will', 'alexa', 'very', 'really', 'use', 'out', 'just', 'would', 'amazon', 'dot', 'when', 'no', 'he', 'me', 'get', 'still'])  # Define your set of stop words\n",
    "\n",
    "# Define a function for text preprocessing\n",
    "def preprocess_text(text):\n",
    "    # Convert to lowercase and split into words\n",
    "    words = text.lower().split()\n",
    "    # Remove stop words and non-alphabetic words\n",
    "    words = [word for word in words if word not in stop_words and word.isalpha()]\n",
    "    # Join words back into a string\n",
    "    preprocessed_text = ' '.join(words)\n",
    "    return preprocessed_text\n",
    "\n",
    "# Apply text preprocessing to the 'verified_reviews' column in the DataFrame\n",
    "df['preprocessed_reviews'] = df['verified_reviews'].apply(preprocess_text)\n",
    "\n",
    "# Create a TF-IDF vectorizer to convert text data into a numerical representation\n",
    "vectorizer = TfidfVectorizer()\n",
    "tfidf_matrix = vectorizer.fit_transform(df['preprocessed_reviews'])\n",
    "\n",
    "# Train the NMF model\n",
    "num_topics = 10  # Number of topics to extract\n",
    "nmf_model = NMF(n_components=num_topics, random_state=1)\n",
    "nmf_model.fit(tfidf_matrix)\n",
    "\n",
    "# Get the topic distribution for each document\n",
    "doc_topics = nmf_model.transform(tfidf_matrix)\n",
    "\n",
    "# Print the topics and their corresponding keywords\n",
    "feature_names = vectorizer.get_feature_names()\n",
    "for i, topic_weights in enumerate(nmf_model.components_):\n",
    "    print(f\"Topic {i+1}:\")\n",
    "    topic_keywords = [feature_names[index] for index in topic_weights.argsort()[:-11:-1]]\n",
    "    print(topic_keywords)\n",
    "    print(\"\\n\")\n",
    "\n",
    "# Get the most dominant topic for each document\n",
    "dominant_topic = doc_topics.argmax(axis=1)\n",
    "df['dominant_topic'] = dominant_topic\n",
    "\n",
    "# Extract the dominant topic and its probability for each document\n",
    "df['dominant_topic_prob'] = doc_topics.max(axis=1)\n",
    "\n",
    "# Get the topic names for each topic ID\n",
    "topic_names = []\n",
    "for topic_id in dominant_topic:\n",
    "    topic_keywords = [feature_names[index] for index in nmf_model.components_[topic_id].argsort()[:-11:-1]]\n",
    "    topic_names.append(topic_keywords[0])\n",
    "\n",
    "# Add the topic names to the DataFrame\n",
    "df['dominant_topic_name'] = topic_names\n",
    "\n",
    "# Display the results\n",
    "print(df[['verified_reviews', 'dominant_topic', 'dominant_topic_name']])\n"
   ],
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      "text/plain": "      rating       date         variation  \\\n0          5  31-Jul-18  Charcoal Fabric    \n1          5  31-Jul-18  Charcoal Fabric    \n2          4  31-Jul-18    Walnut Finish    \n3          5  31-Jul-18  Charcoal Fabric    \n4          5  31-Jul-18  Charcoal Fabric    \n...      ...        ...               ...   \n3145       5  30-Jul-18        Black  Dot   \n3146       5  30-Jul-18        Black  Dot   \n3147       5  30-Jul-18        Black  Dot   \n3148       5  30-Jul-18        White  Dot   \n3149       4  29-Jul-18        Black  Dot   \n\n                                       verified_reviews  \\\n0                                         Love my Echo!   \n1                                             Loved it!   \n2     Sometimes while playing a game, you can answer...   \n3     I have had a lot of fun with this thing. My 4 ...   \n4                                                 Music   \n...                                                 ...   \n3145  Perfect for kids, adults and everyone in betwe...   \n3146  Listening to music, searching locations, check...   \n3147  I do love these things, i have them running my...   \n3148  Only complaint I have is that the sound qualit...   \n3149                                               Good   \n\n                                   preprocessed_reviews  dominant_topic  \\\n0                                                  love               0   \n1                                                 loved               9   \n2     sometimes while playing answer question correc...               5   \n3     had lot fun yr old learns about control lights...               6   \n4                                                 music               4   \n...                                                 ...             ...   \n3145                            perfect adults everyone               9   \n3146  listening searching checking looking there man...               4   \n3147  do love these them running entire all front do...               4   \n3148  only complaint sound quality mostly commands a...               6   \n3149                                               good               7   \n\n      dominant_topic_prob dominant_topic_name  \n0                0.271407                love  \n1                0.021389          everything  \n2                0.051940                like  \n3                0.071706               sound  \n4                0.099493               music  \n...                   ...                 ...  \n3145             0.011501          everything  \n3146             0.035189               music  \n3147             0.070621               music  \n3148             0.073713               sound  \n3149             0.372492                good  \n\n[3150 rows x 8 columns]",
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