Abstract
There is an increasing popularity of people posting their feel-ings on microblogging such as Twitter. Sentiment analysis on the tweets allows organizations to monitor public' feelings towards a product or brand. In this paper, we model sentiment analysis problem as a multi-classcation approach that utilizes various feature types, including predicate-argument relation, hashtag, mention and emotion in the tweets. We describe a Content-Structure Correspondence (CSC) model that is able to represent diverse feature types in a tweet. We present a conceptual hierarchy to express the characteristics of a tweet. A multi-classsifcation approach is used to map tweet content to the conceptual hierarchy. The mapping patterns are learned to identify the sentiment of a tweet.
Original language | English |
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Title of host publication | Web-KR '14: Proceedings of the 5th International Workshop on Web-scale Knowledge Representation Retrieval & Reasoning |
Pages | 19-22 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 3 Nov 2014 |
Externally published | Yes |
Event | International Workshop on Web-scale Knowledge Representation Retrieval and Reasoning 2014 - Shanghai, China Duration: 3 Nov 2014 → 3 Nov 2014 Conference number: 5th https://dl.acm.org/doi/proceedings/10.1145/2663792 (Proceedings) |
Conference
Conference | International Workshop on Web-scale Knowledge Representation Retrieval and Reasoning 2014 |
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Abbreviated title | Web-KR 2014 |
Country/Territory | China |
City | Shanghai |
Period | 3/11/14 → 3/11/14 |
Internet address |
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Keywords
- Multi-classification
- Predicate-argument relation
- Sentiment analysis