Name
Abstract | ||
|---|---|---|
The emergence of new social media such as blogs, message boards, news, and web content in general has dramatically changed the ecosystems of corporations. Consumers, non-profit organizations, and other forms of communities are extremely vocal about their opinions and perceptions on companies and their brands on the web. The ability to leverage such "voice of the web" to gain consumer, brand, and market insights can be truly differentiating and valuable to today’s corporations. In particular, one important form of insights can be derived from sentiment analysis on web content. Sentiment analysis traditionally emphasizes on classification of web comments into positive, neutral, and negative categories. This paper goes beyond sentiment classification by focusing on techniques that could detect the topics that are highly correlated with the positive and negative opinions. Such techniques, when coupled with sentiment classification, can help the business analysts to understand both the overall sentiment scope as well as the drivers behind the sentiment. In this paper, we describe our overall sentiment analysis system that consists of such sentiment analysis techniques. We then detail a novel topic detection method using point-wise mutual information and term frequency distribution. We demonstrate the effectiveness of our overall approaches via several case studies on different social media data sets. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.3233/WIA-2010-0192 | Web Intelligence and Agent Systems: An International Journal |
Keywords | Field | DocType |
pointwise mutual information,sentiment analysis | Data mining,World Wide Web,Social media,Leverage (finance),Information retrieval,Sentiment analysis,Computer science,Message boards,Mutual information,Web content,Perception,The Internet | Conference |
Volume | Issue | Citations |
8 | 3 | 10 |
PageRank | References | Authors |
0.72 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keke Cai | 1 | 243 | 15.36 |
| Scott Spangler | 2 | 162 | 27.58 |
| ying chen | 3 | 134 | 17.07 |
| Li Zhang | 4 | 392 | 20.72 |