Deep Learning Applications in Natural Language Processing for Sentiment Analysis: Unlocking Consumer Behavior Trends

Sentiment analysis is one of the most crucial applications in the Natural Language Processing (NLP) which helps organization or individuals to understand trends about consumers by analyzing the text data. In this research, the use of a mixed deep learning model that incorporates BERT, BiLSTM, and attention is evaluated for sentiment analysis improvement. In this study, the proposed model incorporates BERT embeddings, which consider contextual information, and BiLSTM, which captures sequential dependencies; they form a solution for the language characteristics associated with various datasets. They introduced an attention layer in an inferior level to refine feature selecting in order that the model can focus on sentiment-significant keywords. A huge number of experiments were carried and performed using data sets like IMDB, Yelp, and Twitter, and it proved that the model is suitable for the long form and short form text analysis. The new proposed hybrid model demonstrated better accuracy of 94.5% than other conventional models such as Support Vector Machines which has accuracy of 78.5% along with the LSTMs which has accuracy of 85.7%. Explaining the effectiveness of the method, an ablation study showed the significant importance of the attention mechanism and improved dropout penalty. With reference to the computational complexity experienced, the findings showed the innovative possibilities for hybrid deep learning models in sentiment analysis. They are discussed below as follows: This work provides a direction for building robust and accurate sentiment analysis models for mass consumer trends and intents for various uses.