Name
Abstract | ||
|---|---|---|
Public sentiment permeated through social media is usually regarded as an important measure for hot event detecting, policy making and so forth, hence many governments and intelligence agencies have been launching various initiatives to facilitate theories, technologies and systems toward monitoring its fluctuation. Recently, massive new words are created and widely spread in social media, and they pose a great influence on sentiment analysis. Facing this situation, most previous work still just add those new words into sentiment lexicon, none of the existed researches focuses on the role and influence of new words in emotional expression. In this paper, we pay more attention to the influence of new words and propose two novel new words based sentiment analysis methods, named NWLb and NWSA, the former only with the help of lexicon and the latter further incorporates machine learning, which utilize the distinctive role of new words to improve the effectiveness of sentiment analysis in social media. Experiments on real social media dataset demonstrate the effectiveness and performance of our methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ISI.2016.7745470 | 2016 IEEE Conference on Intelligence and Security Informatics (ISI) |
Keywords | Field | DocType |
lexicon based method,machine learning,sentiment analysis,social media | Data mining,Social media,Computer science,Sentiment analysis,Support vector machine,Emotional expression,Lexicon | Conference |
ISBN | Citations | PageRank |
978-1-5090-3866-4 | 1 | 0.35 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chiyu Cai | 1 | 1 | 0.35 |
| Linjing Li | 2 | 39 | 12.91 |
| Daniel Zeng | 3 | 2539 | 286.59 |