Abstract
Opinion mining and sentiment analysis refer to the identification and the aggregation of attitudes or opinions expressed by internet users towards a specific topic. However, due to the limitation in terms of characters (i.e. 140 characters per tweet) and the use of informal language, the state-of-the-art approaches of sentiment analysis present lower performances in Twitter than that when they are applied on longer texts. Moreover, presence of sarcasm makes the task even more challenging. Sarcasm is when a person conveys implicit information, usually the opposite of what is said, within the message he transmits. In this paper we propose a method that makes use of a minimal set of features, yet, efficiently classifies tweets regardless of their topic. We also study the importance of detecting sarcastic tweets automatically, and demonstrate how the accuracy of sentiment analysis can be enhanced knowing which tweets are sarcastic and which are not.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 1594-1597 |
| Number of pages | 4 |
| ISBN (Print) | 9781450338547 |
| DOIs | |
| Publication status | Published - 2015 Aug 25 |
| Event | IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France Duration: 2015 Aug 25 → 2015 Aug 28 |
Other
| Other | IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 |
|---|---|
| Country | France |
| City | Paris |
| Period | 15/8/25 → 15/8/28 |
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ASJC Scopus subject areas
- Computer Science Applications
- Computer Networks and Communications
Cite this
Opinion mining in Twitter : How to make use of sarcasm to enhance sentiment analysis. / Bouazizi, Mondher; Ohtsuki, Tomoaki.
Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Association for Computing Machinery, Inc, 2015. p. 1594-1597.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Opinion mining in Twitter
T2 - How to make use of sarcasm to enhance sentiment analysis
AU - Bouazizi, Mondher
AU - Ohtsuki, Tomoaki
PY - 2015/8/25
Y1 - 2015/8/25
N2 - Opinion mining and sentiment analysis refer to the identification and the aggregation of attitudes or opinions expressed by internet users towards a specific topic. However, due to the limitation in terms of characters (i.e. 140 characters per tweet) and the use of informal language, the state-of-the-art approaches of sentiment analysis present lower performances in Twitter than that when they are applied on longer texts. Moreover, presence of sarcasm makes the task even more challenging. Sarcasm is when a person conveys implicit information, usually the opposite of what is said, within the message he transmits. In this paper we propose a method that makes use of a minimal set of features, yet, efficiently classifies tweets regardless of their topic. We also study the importance of detecting sarcastic tweets automatically, and demonstrate how the accuracy of sentiment analysis can be enhanced knowing which tweets are sarcastic and which are not.
AB - Opinion mining and sentiment analysis refer to the identification and the aggregation of attitudes or opinions expressed by internet users towards a specific topic. However, due to the limitation in terms of characters (i.e. 140 characters per tweet) and the use of informal language, the state-of-the-art approaches of sentiment analysis present lower performances in Twitter than that when they are applied on longer texts. Moreover, presence of sarcasm makes the task even more challenging. Sarcasm is when a person conveys implicit information, usually the opposite of what is said, within the message he transmits. In this paper we propose a method that makes use of a minimal set of features, yet, efficiently classifies tweets regardless of their topic. We also study the importance of detecting sarcastic tweets automatically, and demonstrate how the accuracy of sentiment analysis can be enhanced knowing which tweets are sarcastic and which are not.
UR - http://www.scopus.com/inward/record.url?scp=84962613847&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962613847&partnerID=8YFLogxK
U2 - 10.1145/2808797.2809350
DO - 10.1145/2808797.2809350
M3 - Conference contribution
AN - SCOPUS:84962613847
SN - 9781450338547
SP - 1594
EP - 1597
BT - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
PB - Association for Computing Machinery, Inc
ER -