A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis. Issue 1 (14th August 2020)

• Record Type: Journal Article
• Authors: Ray, Paramita; Chakrabarti, Amlan
• Is Part Of: Applied computing and informatics. Volume 18:Issue 1/2(2022)
• Page Start: 163
• Page End: 178
• Publication Date: 2020-08-14
• Subjects: Opinion mining -- CNN -- Sentiment analysis -- Text mining -- POS tagging; Information science -- Periodicals; Information storage and retrieval systems -- Periodicals; 004
• DOI: 10.1016/j.aci.2019.02.002
• Languages: English
• ISSNs: 2210-8327

Abstract

Abstract : Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.