Abstract
This paper addresses the issue of how personality recognition can be helpful for sentiment analysis. We exploited the corpus for sentiment analysis released for the SEMEVAL 2013, we automatically annotated personality labels by means of an unsupervised system for personality recognition. We validated the automatic annotation on a small set of Twitter users, whose personality types have been collected by means of an online test. Results show that hashtag position and conscientiousness are the best predictors of sentiment in Twitter.
| Original language | English |
|---|---|
| Title of host publication | ESSEM 2013: Emotion and Sentiment in Social and Expressive Media |
| Subtitle of host publication | Proceedings of the First International Workshop on Emotion and Sentiment in Social and Expressive Media: approaches and perspectives from AI (ESSEM 2013) |
| Editors | Cristina Battaglino, Cristina Bosco, Erik Cambria, Rossana Damiano, Viviana Patti, Paolo Rosso |
| Place of Publication | Aachen |
| Publisher | CEUR |
| Pages | 140-147 |
| Number of pages | 8 |
| Publication status | Published - 2013 |
| Externally published | Yes |
| Event | 1st International Workshop on Emotion and Sentiment in Social and Expressive Media, ESSEM 2013 - Turin, Italy Duration: 3 Dec 2013 → 3 Dec 2013 Conference number: 1 |
Publication series
| Name | CEUR Workshop Proceedings |
|---|---|
| Publisher | 1096 |
| Volume | 1096 |
| ISSN (Print) | 1613-0073 |
Conference
| Conference | 1st International Workshop on Emotion and Sentiment in Social and Expressive Media, ESSEM 2013 |
|---|---|
| Abbreviated title | ESSEM 2013 |
| Country/Territory | Italy |
| City | Turin |
| Period | 3/12/13 → 3/12/13 |
Keywords
- Data mining
- Personality recognition
- Sentiment analysis