Exploring Deep Learning Techniques for Sentiment Analysis on IMDb Movie Reviews Dataset

Deep learning methods have changed the way we deal with natural language processing (NLP), and now our models can generate Textual Data almost as well like humans, but it was recent advances that introduced these revolutionized mechanisms for understanding complex human languages. The task of sentiment analysis-identifying the sentiment in text like movie reviews- is a fundamental one for NLP. This paper focuses on utilizing deep learning techniques, more specifically recurrent neural networkswith Long Short-Term Memory (LSTM) cells for sentiment analysis in IMDb movie reviews dataset. The model proposed in this study uses word embeddings [1], LSTM layers which are integrated as the temporality and gives a way to understand temporal dependencies important for analyzing meaning representations of sequential text data for sentiment classification. We start by pre-processing the IMDb data - cleaning and tokenizing text-data, after which we convert words into dense vector representations using pre-trained word embeddings. These embeddings are then processed by the LSTM layers, which helps to understand the context and sequential information [2].