The Movie-Review-Classification_RNN_LSTM_GRU project aims to perform sentiment analysis on movie reviews using recurrent neural network (RNN) variants such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). Sentiment analysis involves determining the sentiment polarity (positive or negative) associated with a given text, in this case, movie reviews.

Sentiment analysis of movie reviews is crucial for understanding audience reactions and predicting movie success. The problem statement for this project involves building a robust deep learning model capable of accurately classifying movie reviews as positive or negative based on the sentiment expressed in the text. This requires training models that can effectively capture movie reviews' semantic meaning and context, considering factors such as sarcasm, irony, and ambiguity.

The project utilizes the following tools and technologies:

• Python: The primary programming language for implementing the deep learning models and data preprocessing tasks.
• TensorFlow and Keras: Deep learning frameworks used for building and training the RNN variants (LSTM and GRU) for sentiment analysis.
• Pandas and NumPy: Libraries for data manipulation and preprocessing, including handling structured data and numerical operations.
• Matplotlib and Seaborn: Data visualization libraries for generating plots and visualizing model performance metrics.
• Word Embeddings: Pre-trained word embeddings such as Word2Vec or GloVe are used to represent words as dense vectors, capturing semantic relationships between words.

The following data preprocessing techniques are employed in this project:

• Tokenization: Breaking down the text of movie reviews into individual words or tokens to prepare it for input to the deep learning models.
• Padding: Ensuring all sequences have the same length by adding padding tokens, which is necessary for batch processing in the deep learning models.
• Word Embedding: Converting words into dense vector representations using pre-trained word embeddings to capture semantic information.
• Data Splitting: Dividing the dataset into training, validation, and test sets to train and evaluate the models effectively while preventing overfitting.

The project utilizes pre-trained word embeddings such as Word2Vec or GloVe to represent words as dense vectors. These embeddings capture semantic relationships between words and improve the model's performance in understanding textual data. By leveraging pre-trained embeddings, the model can learn from a larger corpus of text data and benefit from transfer learning.

Various optimization algorithms are used to train the deep learning models effectively, including:

• Adam: An adaptive learning rate optimization algorithm that updates the network weights based on the gradients of the loss function.
• RMSprop: Another adaptive learning rate optimization algorithm that maintains a moving average of the squared gradients, which can help train deep learning models more efficiently.

Cross-validation techniques such as k-fold evaluate the model's performance and ensure robustness. By training and testing the model on different subsets of the data, cross-validation provides a more reliable estimate of the model's performance and helps prevent overfitting.

The project implements RNN variants, including LSTM and GRU, to perform sentiment analysis on movie reviews. These models are trained on labeled movie review data to learn the patterns and associations between words and sentiments. The trained models are then evaluated using accuracy, precision, recall, and F1-score metrics to assess their performance in accurately classifying movie reviews.

The dataset used in this project consists of labeled movie reviews, each classified as either positive or negative based on its sentiment. The dataset is typically sourced from publicly available movie review websites or datasets such as IMDb or Rotten Tomatoes. It may include features such as the review text, the reviewer's rating, and additional metadata.

If you have any difficulty with this project, please contact me using my email, gaddisaolex@gmail.com