Add LSTM-based IMDB sentiment analysis example under NLP

examples/nlp/imdb_lstm_sentiment.py

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+"""
+Title: Sentiment analysis with LSTM on the IMDB dataset
+Author:Madhur Jain
+Date created: 2025/11/19
+Last Modified: 2025/11/19
+Description: A simple LSTM-based sentiment classifier trained on IMDB text reviews.
+"""
+
+"""
+## Introduction
+
+LSTM refers to Long short term memories, that is while predicting it not only keeps short term memory but also long term memory
+LSTM uses sigmoid activation functions and tan-h activation functions:
+    The Sigmoid fn. ranges the values from 0 to 1,
+    tan-h function ranges the values from -1 to 1.
+Doesn't let Gradient Descent of long term memory to vanish or short term memory to completely explode.
+It contains 3 stages: 
+    1st stage: Determines what % of long term memory is remembered- c/a Forget Gate
+    2nd stage: Determines how we would update long-term memory- c/a Input Gate
+    3rd stage: Updates short term memory and it is the output of the entire stage- c/a Output Gate
+
+If you wanna know more deeply about it, I would recommend to watch Stanford Online: statistacl analysis with Python course lectures available on Youtube (for free)
+
+"""
+
+import os
+os.environ["KERAS_BACKEND"] = "tensorflow"
+
+import keras
+import tensorflow as tf
+from keras import layers
+from keras.models import Sequential
+
+"""
+## Load the dataset
+get the kAGGLE.json from your kaggle account->settings->create new token
+"""
+kaggle_dictionary = json.load(open("kaggle.json"))  #converts json object to python dictionary
+#Setup Kaggle collection as env vars
+kaggle_dictionary.keys()
+
+os.environ["KAGGLE_USERNAME"] = kaggle_dictionary["username"]
+os.environ["KAGGLE_KEY"] = kaggle_dictionary["key"]
+
+# unzip the dataset file
+with ZipFile("imdb-dataset-of-50k-movie-reviews.zip", "r") as zip_ref:
+  zip_ref.extractall()
+
+#loading the dataset
+data = pd.read_csv("/content/IMDB Dataset.csv")
+
+data.shape
+
+data.info()
+
+data.head()
+
+data["sentiment"].value_counts()
+
+data.replace({"sentiment": {"positive": 1, "negative": 0}}, inplace=True)
+
+data.head()
+
+data["sentiment"].value_counts()
+
+
+"""
+## Splitting into Training and test set 
+"""
+train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)
+
+print(train_data.shape)
+print(test_data.shape)
+
+
+"""
+## Data Processing
+"""
+#Tokenize text data
+# for text data one have to tokenize(convert words to integer in short) the data and stuff
+tokenizer = Tokenizer(num_words=5000)
+tokenizer.fit_on_texts(train_data["review"])
+X_train = pad_sequences(tokenizer.texts_to_sequences(train_data["review"]), maxlen=200)
+X_test = pad_sequences(tokenizer.texts_to_sequences(test_data["review"]), maxlen=200)
+
+print(X_train)
+print(X_test)
+
+Y_train = train_data["sentiment"]
+Y_test = test_data["sentiment"]
+
+print(Y_train)
+print(Y_test)
+
+
+"""
+## LSTM (Long Short Term Memory) Model
+"""
+# build the model
+
+model = Sequential()  #sequential model
+#add layers
+model.add(Embedding(input_dim=5000, output_dim=128, input_shape=(200,)))
+model.add(LSTM(128, dropout=0.2))
+model.add(Dense(1, activation="sigmoid"))
+
+model.summary()
+
+# compile the model
+model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
+"""
+## Training the Model
+"""
+model.fit(X_train, Y_train, epochs=5, batch_size=64, validation_split=0.2)
+
+"""
+## Model Evaluation
+"""
+loss, accuracy = model.evaluate(X_test, Y_test)
+print(f"Test Loss: {loss}")
+print(f"Test Accuracy: {accuracy}")
+
+"""
+### Predicting Values
+"""
+def predict_sentiment(review):
+  # tokenize and pad the review
+  sequence = tokenizer.texts_to_sequences([review])
+  padded_sequence = pad_sequences(sequence, maxlen=200)
+  prediction = model.predict(padded_sequence)
+  sentiment = "positive" if prediction[0][0] > 0.5 else "negative"
+  return sentiment
+
+
+#  examples
+
+new_review = "This movie was fantastic. I loved it."
+sentiment = predict_sentiment(new_review)
+print(f"The sentiment of the review is: {sentiment}")
+#===================================================================================#
+new_review = "This movie was not that good"
+sentiment = predict_sentiment(new_review)
+print(f"The sentiment of the review is: {sentiment}")
+#===================================================================================#
+new_review = "Great movie but could have added a better action scene"
+sentiment = predict_sentiment(new_review)
+print(f"The sentiment of the review is: {sentiment}")
+#===================================================================================#
+new_review = "Mid movie"
+sentiment = predict_sentiment(new_review)
+print(f"The sentiment of the review is: {sentiment}")
+# ==================================================================================#
+new_review = "I laughing while shitting damn what a watch"
+sentiment = predict_sentiment(new_review)
+print(f"The sentiment of the review is: {sentiment}")