Aspect-Based Sentiment Analysis
A Survey of Deep Learning Methods
Bibliographic Data
| ID | 22108316 |
|---|---|
| Authors | Haoyue Liu (0000-0003-1379-7648, New Jersey Institute of Technology), Ishani Chatterjee (0000-0003-1145-7586, New Jersey Institute of Technology), Mengchu Zhou (0000-0002-5408-8752, New Jersey Institute of Technology), Xiaoyu Sean Lu (0000-0002-0692-2247, New Jersey Institute of Technology), Abdullah Abusorrah (0000-0001-8025-0453, King Abdulaziz University) |
| Year | 2020 |
| Volume | 7 |
| Issue | 6 |
| Pages | 1358-1375 |
| Publication date | 2020-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2020.3033302 |
| OpenAlex | W3098363382 |
| Language | EN |
| Citations received | 19 |
| References cited | 4 |
Sentiment analysis is a process of analyzing, processing, concluding, and inferencing subjective texts with the sentiment. Companies use sentiment analysis for understanding public opinion, performing market research, analyzing brand reputation, recognizing customer experiences, and studying social media influence. According to the different needs for aspect granularity, it can be divided into document, sentence, and aspect-based ones. This article summarizes the recently proposed methods to solve an aspect-based sentiment analysis problem. At present, there are three mainstream methods: lexicon-based, traditional machine learning, and deep learning methods. In this survey article, we provide a comparative review of state-of-the-art deep learning methods. Several commonly used benchmark data sets, evaluation metrics, and the performance of the existing deep learning methods are introduced. Finally, existing problems and some future research directions are presented and discussed
Data science · Deep learning · Granularity · Lexicon · Machine learning · Mainstream · Market research · Natural language processing · Public opinion · Reputation · Sentence · Sentiment analysis · Advanced Text Analysis Techniques · Computer Science · Sentiment Analysis and Opinion Mining · Text and Document Classification Technologies · Artificial Intelligence
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| Unique citing works | 19 |
|---|---|
| Citations per year | 3.8 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 19 |