A Manifold-Level Hybrid Deep Learning Approach for Sentiment Classification Using an Autoregressive Model
Source abstract
Abstract
With the recent expansion of social media in the form of social networks, online portals, and microblogs, users have generated a vast number of opinions, reviews, ratings, and feedback. Businesses, governments, and individuals benefit greatly from this information. While this information is intended to be informative, a large portion of it necessitates the use of text mining and sentiment analysis models. It is a matter of concern that reviews on social media lack text context semantics. A model for sentiment classification for customer reviews based on manifold dimensions and manifold modeling is presented to fully exploit the sentiment data provided in reviews and handle the issue of the absence of text context semantics. This paper uses a deep learning framework to model review texts using two dimensions of language texts and ideogrammatic icons and three levels of documents, sentences, and words for a text context semantic analysis review that enhances the precision of the sentiment categorization process. Observations from the experiments show that the proposed model outperforms the current sentiment categorization techniques by more than 8.86%, with an average accuracy rate of 97.30%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sentiment Analysis and Opinion Mining · Advanced Text Analysis Techniques · Topic Modeling
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science and Engineering, KIPM College of Engineering and Technology, Gorakhpur 273209, India
Department of Computer Science and Engineering, Krishna Institute of Engineering and Technology, Ghaziabad 201206, India
Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur 273016, India
Department of Computer Science and Engineering, Amity School of Engineering and Technology Lucknow, Amity University Uttar Pradesh, Noida 201301, India
Department of Computer Science and Engineering & IT, Jaypee Institute of Information Technology, Noida 201309, India
Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi 110086, India
Department of Electronics and Communication Engineering, IcfaiTech (Faculty of Science and Technology), IFHE University, Hyderabad 500029, India
School of Computer Science & Engineering (SCOPE), VIT-AP University, Amaravati 522237, India
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