Enhanced sentiment analysis framework: Ensemble attention enhanced bidirectional long-short-term encoder for accurate classification of consumer reviews

The fast growth of today's technology on social media, the web, etc caused the ongoing and fast creation of opinionated textual data. In this context, internet reviews by consumers determine upcoming consumers whether can buy a particular product or not. Because the thoughts regarding the products are expressed by commenting on reviews. The sentiment analysis is more supportive for comprehending feedback and the attitude of consumers regarding the product’s characteristics. More sentiment classification tasks are carried out by the researchers but attained low level output due to difficulty in learning contextual words. To rectify this type of issue, current work focuses on developing a sentiment analysis framework by progressive techniques. The novel namely Ensemble Attention Enhanced Bidirectional Long-short-term Encoder (EAE-BiLE) is developed. The developed method analyzes online reviews and gains reviews from Amazon and the second task is to form high-quality input. The BERT model generates contextual word embeddings and Bi-directional Long-Short-Term Memory (Bi-LSTM) processes these embeddings in order to capture both backward and forward dependencies from the text. The novel method makes the accurate classification by focusing on important and relevant parts in the text using an attention mechanism that assigns different weights. Detailed experiments that expose the performance of the developed method are performed in this work. This requires a few evaluation metrics that can measure the effectiveness of the developed method, which is accomplished by F1-score, recall, accuracy, and precision. The developed EAE-BiLE framework yields higher rates of 97.2 %, 97.2 %, 99.1 %, and 98.4 % for precision, F1-score, accuracy, and recall. Its error performance is also low, which represents the developed method makes the precise classification. The developed EAE-BiLE excels with higher effectiveness than prior techniques for efficacious sentiment classification.