Attention Enhanced Deep Neural Network with Effective Embedding Techniques for Sentiment Analysis

Abstract

With the growth of the Internet and the existence of various social media, people newlinehave opportunities to share their thoughts on topics viz., social, financial, political, newlinereligious, entertainment, etc. User-generated reviews on products are expanding rapidly newlinewith the emergence of e-commerce. These reviews are valuable to business organizations newlinefor improving their products and to individual consumers for making decisions about newlinethe entity. These online platforms consist of valuable information and an enormous newlineamount of data for sentiment analysis. It is very difficult to handle the sentiment newlineanalysis manually. An effective and advanced data analytic technique is required to newlineextract intelligent opinionated content from the enormous amount of data. newlineIdentifying and detecting opinionated content play a major role in taking an important newlinebusiness decision. Sentiment terms do not have an explicit definition and they are newlinewritten in user convenient language. Hence, it is difficult to identify and categorize newlinethese contents. Influencing terms may spread across a sentence that contributes to the newlinepolarity of the tweets. These terms must be identified, related and weighted with other newlineterms in a sentence. The relative terms that are distributed across multiple sentences newlineof the review, which influence the sentiment of the review are also not considered. A newlineword may have a different meaning in various contexts and there are very few works that newlinefocus on identifying the opinionated content in the early stages. A shallow deep learning newlinemodel cannot categorize opinionated terms. A deep contextual model is required to find newlinethe opinionated words from a review having multiple meanings or relations with other newlinewords. To handle the above issues in the sentiment analysis task, we have developed newlinedifferent architectures. newlineHybrid Convolution Bidirectional Recurrent Neural Network (CBRNN) is introduced newlineto overcome the issue related to identifying contextual terms. Word Embedding and newlineSelf-Attention are combined with Long Short Term Memory (WE-SALSTM)