Name
Abstract | ||
|---|---|---|
We present a generic approach to real-time monitoring of the Twitter sentiment and show its application to the Bulgarian parliamentary elections in May 2013. Our approach is based on building high quality sentiment classification models from manually annotated tweets. In particular, we have developed a user-friendly annotation platform, a feature selection procedure based on maximizing prediction accuracy, and a binary SVM classifier extended with a neutral zone. We have also considerably improved the language detection in tweets. The evaluation results show that before and after the Bulgarian elections, negative sentiment about political parties prevailed. Both, the volume and the difference between the negative and positive tweets for individual parties closely match the election results. The later result is somehow surprising, but consistent with the prevailing negative sentiment during the elections. |
Year | Venue | Field |
|---|---|---|
2015 | PROCEEDINGS OF THE 2015 IEEE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (IEEE DSAA 2015) | Data mining,Annotation,Bulgarian,Feature selection,Computer science,Sentiment analysis,Artificial intelligence,Natural language processing,Language identification,Svm classifier,General election |
DocType | Citations | PageRank |
Conference | 8 | 0.52 |
References | Authors | |
23 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jasmina Smailovic | 1 | 145 | 7.80 |
| Janez Kranjc | 2 | 81 | 5.14 |
| Miha Grcar | 3 | 224 | 15.71 |
| Martin Znidarsic | 4 | 133 | 11.51 |
| Igor Mozetic | 5 | 608 | 198.93 |