Name
Abstract | ||
|---|---|---|
Recently, there has been growing research interest in the sentiment analysis of tweets. However, there is still a need to examine the contribution of Twitter-specific features to this task. One such feature is hashtags, which are user-defined topics. In our study, we compare the performance of sentiment and non-sentiment hashtags in classifying tweets as positive or negative. By combining subjective words from different lexical resources, we achieve accuracy scores of 83.58% and 83.83% in identifying sentiment hashtags and non-sentiment hashtags, respectively. Furthermore, our accuracy scores surpass those scores obtained using models that apply a single lexical resource. We apply derived properties of sentiment and non-sentiment hashtags, including their sentiment polarity to classify tweets. Our best classification models achieve accuracy scores of 81.14% and 86.07% using sentiment hashtags and non-sentiment hashtags, respectively. Additionally, our models perform comparably to supervised machine learning algorithms, and outperform a scoring algorithm developed in a previous study. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-24282-8_21 | DISCOVERY SCIENCE, DS 2015 |
Field | DocType | Volume |
Sentiment analysis,Scoring algorithm,Computer science,Artificial intelligence,Machine learning | Conference | 9356 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.38 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Credell Simeon | 1 | 3 | 0.73 |
| Robert J. Hilderman | 2 | 270 | 29.86 |