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DL_Sentiment_Analysis

Aim

The goal of this project is to develop a machine learning model capable of accurately classifying movie reviews as either positive or negative. By leveraging a recurrent neural network (RNN), the project aims to handle sequential text data, specifically from movie reviews, to identify the sentiment expressed by the reviewer.

Dataset

The dataset utilized in this project is the "IMDB Movie Review Dataset," which comprises a collection of movie reviews labeled as positive or negative. This dataset is a standard benchmark in NLP and sentiment analysis studies, making it ideal for training and evaluating our model.

Machine Learning Algorithms

To tackle the sentiment analysis task, the following machine learning algorithms and techniques were employed:

  • Recurrent Neural Networks (RNN): Utilized for their efficacy in processing sequences of text by capturing the contextual relationships in sentences.
  • Long Short-Term Memory (LSTM): A specific type of RNN used to avoid the long-term dependency problem, allowing the model to learn and remember over longer sequences.
  • Transfer Learning: Leveraging pre-trained models to enhance the performance and training efficiency of our LSTM model.
  • BERT Model

Please contact me for the model output and dataset access!

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