An Aspect-Based Sentiment Analysis Model to Classify the Sentiment of Twitter Data Using Long-Short Term Memory Classifier

Sentiment analysis of Twitter data is an advanced automated technique used to examine the large amount of textual data that is communicated through tweets with the goal of understanding the public's sentiment and opinion. This strategy is essential in a wide range of fields, including political campaigns and commercial initiatives. Still, sentiment analysis remains a difficult task due to the ongoing difficulty in accurately identifying the polarities of language inside tweets. Our research presents a novel method called Aspect Based Sentiment Analysis (ABSA) to tackle this difficulty. ABSA's main goal is to extract the text's underlying contextual meaning so that sentiment classification may be done with greater accuracy. This methodology makes use of advanced feature extraction techniques, such as Time Frequency Inverse Document Frequency (TF-IDF) and Bag of Words (BOW). These characteristics play a crucial role in enabling the aspect-based sentiment analysis that follows, making it easier to identify the important elements that influence the text's overall sentiment. By identifying and classifying relevant textual characteristics, the aspect-based sentiment analysis penetrates deeper, revealing the subtle aspects of the sentiment. The contextual words are classified using Long-Short Term Memory (LSTM), a potent deep learning architecture, in order to further improve accuracy. This complex stage, which divides sentiment into positive, neutral, and negative groups, is essential to obtaining a detailed categorization. The evaluation shows that the proposed ABSA model with LSTM performs better in terms of classification accuracy than previous methods. This success is significantly better than the results of other approaches, like Binary Brain Storm Optimisation and Fuzzy Cognitive Maps (BBSO-FCM) and Stochastic Gradient Descent optimization based on Stochastic Gate Neural Network (SGD-SGNN). These results highlight the effectiveness and superiority of our suggested ABSA model, which makes it an effective method for sentiment research within the Twitter verse and may have wider use in other textual data sources.