Abstract:
Sentiment analysis, also known as opinion mining, is the process of extracting subjective information from text. It is a crucial task in natural language processing, as it allows for the automatic interpretation of the sentiment expressed in a piece of text. In this paper, we propose a method for sentiment analysis using bi-directional long short-term memory (Bidirectional LSTM) networks. LSTM networks are a type of recurrent neural network that are well-suited to working with sequential data, such as text. By using a bi-directional LSTM, we are able to consider both the past and future context of a word, allowing for more accurate sentiment prediction. Our experimental results show that the proposed method outperforms several strong baselines on a sentiment analysis benchmark dataset. We have achieved 91% accuracy.
