MiSAS: a multi-domain feature-level sentiment analysis system on micro-blog
Authors: 
Chao Zhang, Hui Song, Zhenyu LiuAuthors Info & Claims
Chao Zhang, Hui Song, Zhenyu LiuAuthors Info & ClaimsPages 14 - 18
Abstract
Big data from micro-blog has been an important access to social groups' psychology, market feedback and so on. Unlike the review corpus which is usually related to the specific object (e.g. a product), the micro-blog content covers the opinion of many domains. It is less useful to extract the fine-grained feature-level opinion target without detect the domain. This paper proposed a systematic feature-level sentiment analysis approach on Micro-blog that recognize data related to the interesting topic automatically. Working with the big micro-blog data we figure out valuable text features to train the opinion targets extraction and sentimental polarity detection models that achieve better multi-domain adaption. We implement the MiSAS system, which crawls micro-blog raw data, outputs opinion targets and orientation summarization on the giving domains, offering valuable analytical tool for practical applications.
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- MiSAS: a multi-domain feature-level sentiment analysis system on micro-blog
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Information & Contributors
Information
Published In
November 2017
545 pages
ISBN:9781450353656
DOI:10.1145/3162957
- Conference Chairs:
- Jalel Ben-Othman,
- Feng Gang,
- Program Chairs:
- Jain-Shing Liu,
- Masayuki Arai
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Association for Computing Machinery
New York, NY, United States
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Published: 24 November 2017
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ICCIP 2017
ICCIP 2017: 2017 the 3rd International Conference on Communication and Information Processing
November 24 - 26, 2017
Tokyo, Japan
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Overall Acceptance Rate 61 of 301 submissions, 20%
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