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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.9 No.6
- 발행연도
- 2020.12
- 수록면
- 435 - 444 (10page)
- DOI
- 10.5573/IEIESPC.2020.9.6.435
이용수
초록· 키워드
오류제보하기
In recent years, Sentiment Analysis is reshaping the business operations of many organizations by monitoring their brand reputation on social media and acquiring insights from customer"s feedback. Sentiment Analysis is one of the classification tools that identifies and extracts the subjective information of a product. This subjective information can be stated in different ways, such as feedback, discussions, blogs, podcasts, and video logs. This type of information generated by the empowered customers is known as user-generated content, which is traditionally in the form of words. The analysis was performed on a huge number of words using Natural Language Processing (NLP), which is a Unimodal Sentiment Analysis. With the rapid growth in the usage of the Internet, social media turned out to be a platform to share the thoughts of the individuals. This caused researchers to migrate from the traditional Unimodal analysis to Multimodal Sentiment Analysis, which includes video, audio, and images. This approach leverages the use of emotion and content and helps identify the scope and polarity of an individual’s sentiment. With the latest deep learning algorithms, Multimodal Sentiment Analysis can solve the problem of sarcasm identification. Multi-Modal Sentiment Analysis generates more accurate results compared to Uni Modal Sentiment Analysis. Therefore, this study aimed to define Sentiment Analysis and review the approaches and techniques in Sentiment Analysis from conventional Unimodal to Multimodal. In addition, this paper discusses a Multimodal Sentiment Analysis architecture using a transformers attention net.
목차
Abstract
1. Introduction
2. Problem Definition and Approach
3. Related Computational Work on Sentiment Analysis
4. Motivation
5. Framework for Multimodal Sentiment Analysis using Transformers
6. Conclusion
References
참고문헌 (41)
참고문헌 신청
Ravi, K / 2015 / A survey on opinion mining and sentiment analysis: tasks, approaches and applications / Knowledge-Based Systems 89:14~46
M. S. O'Hern / 2013 / The Empowered Customer : User-Generated Content and the Future of Marketing / Glob. Econ. Manag. Rev 18(1):22~30
Soleymani, M / 2017 / A survey of multimodal sentiment analysis / Image and Vision Computing 65:3~14
Majumder, N / 2018 / Multimodal sentiment analysis using hierarchical fusion with context modeling / Knowledge-based systems 161:124~133
M. V. Mäntylä / 2018 / The evolution of sentiment analysis-A review of research topics, venues, and top cited papers / Comput. Sci. Rev 27:16~32
M. S. O'Hern / 2013 / The Empowered Customer : User-Generated Content and the Future of Marketing / Glob. Econ. Manag. Rev 18(1):22~30
Soleymani, M / 2017 / A survey of multimodal sentiment analysis / Image and Vision Computing 65:3~14
Majumder, N / 2018 / Multimodal sentiment analysis using hierarchical fusion with context modeling / Knowledge-based systems 161:124~133
M. V. Mäntylä / 2018 / The evolution of sentiment analysis-A review of research topics, venues, and top cited papers / Comput. Sci. Rev 27:16~32
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