Tweet Sentiment Analytics with Context Sensitive Tone-Word Lexicon
Title | Tweet Sentiment Analytics with Context Sensitive Tone-Word Lexicon |
Publication Type | Conference Paper |
Year of Publication | 2014 |
Authors | Babour, A., Khan, J.I. |
Conference Name | Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014 IEEE/WIC/ACM International Joint Conferences on |
Date Published | Aug |
Keywords | Accuracy, Cameras, Context, context sensitive tone lexicon learning mechanism, data mining, Dictionaries, learning (artificial intelligence), natural language processing, opinion polarity mining, Semantics, sentiment analysis, social networking (online), text analysis, tone word polarity, tweet sentiment analytics, twitter sentiment analytics, word processing |
Abstract | In this paper we propose a twitter sentiment analytics that mines for opinion polarity about a given topic. Most of current semantic sentiment analytics depends on polarity lexicons. However, many key tone words are frequently bipolar. In this paper we demonstrate a technique which can accommodate the bipolarity of tone words by context sensitive tone lexicon learning mechanism where the context is modeled by the semantic neighborhood of the main target. Performance analysis shows that ability to contextualize the tone word polarity significantly improves the accuracy. |
DOI | 10.1109/WI-IAT.2014.61 |
Citation Key | 6927570 |
- opinion polarity mining
- word processing
- twitter sentiment analytics
- tweet sentiment analytics
- tone word polarity
- text analysis
- social networking (online)
- sentiment analysis
- Semantics
- Accuracy
- natural language processing
- learning (artificial intelligence)
- Dictionaries
- Data mining
- context sensitive tone lexicon learning mechanism
- Context
- Cameras