A hybrid optimization algorithm using BiLSTM structure for sentiment analysis

Sentiment analysis can assist consumers in providing clear and objective sentiment recommendations based on large amounts of data, and it is helpful in overcoming unclear human flaws in subjective assessments. Existing sentiment analysis methods, on the other hand, must be enhanced in terms of robustness and accuracy. To improve marketing strategies based on product reviews, a reliable mechanism for forecasting sentiment polarity should be implemented. This paper proposes a new approach for sentiment analysis called Taylor–Harris Hawks Optimization driven long short-term memory (THHO- BiLSTM). By incorporating Taylor series in HHO, Taylor–HHO is formed, which aids in improving the BiLSTM classifier's performance by picking optimal weights in the hidden layers. The proposed method was evaluated using Amazon product reviews and reviews from the Taboada corpus benchmark datasets, yielding findings with 96.93% and 93% accuracy, respectively. When compared to existing approaches, the suggested model exceeds them in terms of accuracy. The proposed approach helps manufacturers improve their products based on user feedback.