A Manifold-Level Hybrid Deep Learning Approach for Sentiment Classification Using an Autoregressive Model
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/5/3091 |
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| author | Roop Ranjan Dilkeshwar Pandey Ashok Kumar Rai Pawan Singh Ankit Vidyarthi Deepak Gupta Puranam Revanth Kumar Sachi Nandan Mohanty |
| author_facet | Roop Ranjan Dilkeshwar Pandey Ashok Kumar Rai Pawan Singh Ankit Vidyarthi Deepak Gupta Puranam Revanth Kumar Sachi Nandan Mohanty |
| author_sort | Roop Ranjan |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-11T07:30:57Z |
| format | Article |
| id | doaj.art-a6c0ab8a2cab42559dc4cc22fe57704d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T07:30:57Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a6c0ab8a2cab42559dc4cc22fe57704d2023-11-17T07:19:09ZengMDPI AGApplied Sciences2076-34172023-02-01135309110.3390/app13053091A Manifold-Level Hybrid Deep Learning Approach for Sentiment Classification Using an Autoregressive ModelRoop Ranjan0Dilkeshwar Pandey1Ashok Kumar Rai2Pawan Singh3Ankit Vidyarthi4Deepak Gupta5Puranam Revanth Kumar6Sachi Nandan Mohanty7Department of Computer Science and Engineering, KIPM College of Engineering and Technology, Gorakhpur 273209, IndiaDepartment of Computer Science and Engineering, Krishna Institute of Engineering and Technology, Ghaziabad 201206, IndiaDepartment of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur 273016, IndiaDepartment of Computer Science and Engineering, Amity School of Engineering and Technology Lucknow, Amity University Uttar Pradesh, Noida 201301, IndiaDepartment of Computer Science and Engineering & IT, Jaypee Institute of Information Technology, Noida 201309, IndiaDepartment of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi 110086, IndiaDepartment of Electronics and Communication Engineering, IcfaiTech (Faculty of Science and Technology), IFHE University, Hyderabad 500029, IndiaSchool of Computer Science & Engineering (SCOPE), VIT-AP University, Amaravati 522237, IndiaWith 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%.https://www.mdpi.com/2076-3417/13/5/3091autoregressive modelcustomer reviewsdeep learningemotion analysisoptimized classification |
| spellingShingle | Roop Ranjan Dilkeshwar Pandey Ashok Kumar Rai Pawan Singh Ankit Vidyarthi Deepak Gupta Puranam Revanth Kumar Sachi Nandan Mohanty A Manifold-Level Hybrid Deep Learning Approach for Sentiment Classification Using an Autoregressive Model Applied Sciences autoregressive model customer reviews deep learning emotion analysis optimized classification |
| title | A Manifold-Level Hybrid Deep Learning Approach for Sentiment Classification Using an Autoregressive Model |
| title_full | A Manifold-Level Hybrid Deep Learning Approach for Sentiment Classification Using an Autoregressive Model |
| title_fullStr | A Manifold-Level Hybrid Deep Learning Approach for Sentiment Classification Using an Autoregressive Model |
| title_full_unstemmed | A Manifold-Level Hybrid Deep Learning Approach for Sentiment Classification Using an Autoregressive Model |
| title_short | A Manifold-Level Hybrid Deep Learning Approach for Sentiment Classification Using an Autoregressive Model |
| title_sort | manifold level hybrid deep learning approach for sentiment classification using an autoregressive model |
| topic | autoregressive model customer reviews deep learning emotion analysis optimized classification |
| url | https://www.mdpi.com/2076-3417/13/5/3091 |
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