sentiment-analysis-using-NLP.ipynb

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   "source": [
    "# Sentiment Analysis using NLP\n",
    "\n",
    "* Description: Analyze the sentiment of text data, such as tweets, reviews, or comments.\n",
    "* Skills: NLP, machine learning, text processing, TensorFlow/Keras/PyTorch.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Collect text data from sources from imdb dataset.\n",
    "* Preprocess the text (tokenization, stopwords removal, etc.).\n",
    "* Train a sentiment analysis model (e.g., using LSTM or BERT).\n",
    "* Evaluate the model and deploy it as a web service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "import numpy as np\n",
    "\n",
    "# Load dataset (e.g., IMDb reviews)\n",
    "(train_texts, train_labels), (test_texts, test_labels) = tf.keras.datasets.imdb.load_data(num_words=10000)\n",
    "\n",
    "# Preprocess text data\n",
    "tokenizer = Tokenizer(num_words=10000)\n",
    "train_sequences = pad_sequences(train_texts, maxlen=500)\n",
    "test_sequences = pad_sequences(test_texts, maxlen=500)\n",
    "\n",
    "# Build model\n",
    "model = models.Sequential([\n",
    "    layers.Embedding(10000, 128, input_length=500),\n",
    "    layers.LSTM(128),\n",
    "    layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "# Compile model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train model\n",
    "model.fit(train_sequences, train_labels, epochs=10, validation_data=(test_sequences, test_labels))\n"
   ]
  }
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