A Dynamic Weight Function Based BERT Auto Encoder for Sentiment Analysis

A Dynamic Weight Function Based BERT Auto Encoder for Sentiment Analysis

Sentiment analysis is a crucial task in Natural Language Processing (NLP) that determines whether users have positive, neutral, or negative feelings about movies or products. NLP is being employed to address challenges related to the implementation of sentiment analysis. In these issues, basic polarity detection has been replaced by intricate emoticons that differentiate between various negative emotions. Utilizing a feature set to train the classifier, machine learning (ML) techniques can surpass lexicon-based methods in performance, but their effectiveness may be constrained to specific applications. The existing algorithms were processed to reduce the effects on social media and fake news detections but do not work on the classification and validation of tweets. To overcome these issues, a novel Deep Learning model with Weight Function based Bidirectional Encoder Representation from Transformers (WF-BERT) is proposed in this research to achieve high classification accuracy compared to the conventional ML techniques The weight functions of input and output elements in the proposed model are determined dynamically, based on the connection link between input and output. The obtained output results of the proposed model achieved an accuracy of 93.66%, recall of 87.30%, the precision of 88.00%, and F1-score of 87.90%. The proposed model outperforms existing language representation models with limited training data by including additional domain information.