Attention-Based Model for Sentiment Analysis

With the evolution of deep learning, work has been carried out in various fields of NLP. With sentiment analysis, the reviews generated on various platforms can be easily analyzed and could be classified based on the polarity. In this work, IMDB movie dataset has been used and results are compared with the existing baseline techniques. A model is implemented using attention-based mechanism. BiLSTM is used in extracting the global features from the text and for overcoming the gradient issues. This model helps in understanding the contextual relationship in the words just like the human brain. With the use of attention model, the problems of long-term dependencies are resolved thus giving better performance. Evaluation parameters are computed based on the accuracy, precision, recall, and F-score. Results of this model prove to be better than the previous existing techniques