Details
| Original language | English |
|---|---|
| Title of host publication | KDD '17 |
| Subtitle of host publication | Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
| Pages | 1823-1832 |
| Number of pages | 10 |
| ISBN (electronic) | 9781450348874 |
| Publication status | Published - 13 Aug 2017 |
| Event | 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 - Halifax, Canada Duration: 13 Aug 2017 → 17 Aug 2017 |
Abstract
Sentiment analysis is an important task in order to gain insights over the huge amounts of opinions that are generated in the social media on a daily basis. Although there is a lot of work on sentiment analysis, there are no many datasets available which one can use for developing new methods and for evaluation. To the best of our knowledge, the largest dataset for sentiment analysis is TSentiment [8], a 1.6 millions machine-annotated tweets dataset covering a period of about 3 months in 2009. This dataset however is too short and therefore insufficient to study heterogeneous, fast evolving streams. Therefore, we annotated the Twitter dataset of 2015 (228 million tweets without retweets and 275 million with retweets) and we make it publicly available for research. For the annotation we leverage the power of unlabeled data, together with labeled data using semi-supervised learning and in particular, Self-Learning and Co-Training. Our main contribution is the provision of the TSentiment15 dataset together with insights from the analysis, which includes a batch and a stream-processing of the data. In the former, all labeled and unlabeled data are available to the algorithms from the beginning, whereas in the later, they are revealed gradually based on their arrival time in the stream.
Keywords
- Cotraining, Self-learning, Semi-supervised learning, Sentiment analysis
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Information Systems
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KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. p. 1823-1832.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Large Scale Sentiment Learning with Limited Labels
AU - Iosifidis, Vasileios
AU - Ntoutsi, Eirini
N1 - Funding information: The work was partially funded by the European Commission for the ERC Advanced Grant ALEXANDRIA under grant No. 339233 and by the German Research Foundation (DFG) project OSCAR (Opinion Stream Classification with Ensembles and Active leaRners).
PY - 2017/8/13
Y1 - 2017/8/13
N2 - Sentiment analysis is an important task in order to gain insights over the huge amounts of opinions that are generated in the social media on a daily basis. Although there is a lot of work on sentiment analysis, there are no many datasets available which one can use for developing new methods and for evaluation. To the best of our knowledge, the largest dataset for sentiment analysis is TSentiment [8], a 1.6 millions machine-annotated tweets dataset covering a period of about 3 months in 2009. This dataset however is too short and therefore insufficient to study heterogeneous, fast evolving streams. Therefore, we annotated the Twitter dataset of 2015 (228 million tweets without retweets and 275 million with retweets) and we make it publicly available for research. For the annotation we leverage the power of unlabeled data, together with labeled data using semi-supervised learning and in particular, Self-Learning and Co-Training. Our main contribution is the provision of the TSentiment15 dataset together with insights from the analysis, which includes a batch and a stream-processing of the data. In the former, all labeled and unlabeled data are available to the algorithms from the beginning, whereas in the later, they are revealed gradually based on their arrival time in the stream.
AB - Sentiment analysis is an important task in order to gain insights over the huge amounts of opinions that are generated in the social media on a daily basis. Although there is a lot of work on sentiment analysis, there are no many datasets available which one can use for developing new methods and for evaluation. To the best of our knowledge, the largest dataset for sentiment analysis is TSentiment [8], a 1.6 millions machine-annotated tweets dataset covering a period of about 3 months in 2009. This dataset however is too short and therefore insufficient to study heterogeneous, fast evolving streams. Therefore, we annotated the Twitter dataset of 2015 (228 million tweets without retweets and 275 million with retweets) and we make it publicly available for research. For the annotation we leverage the power of unlabeled data, together with labeled data using semi-supervised learning and in particular, Self-Learning and Co-Training. Our main contribution is the provision of the TSentiment15 dataset together with insights from the analysis, which includes a batch and a stream-processing of the data. In the former, all labeled and unlabeled data are available to the algorithms from the beginning, whereas in the later, they are revealed gradually based on their arrival time in the stream.
KW - Cotraining
KW - Self-learning
KW - Semi-supervised learning
KW - Sentiment analysis
UR - http://www.scopus.com/inward/record.url?scp=85029066358&partnerID=8YFLogxK
U2 - 10.1145/3097983.3098159
DO - 10.1145/3097983.3098159
M3 - Conference contribution
AN - SCOPUS:85029066358
SP - 1823
EP - 1832
BT - KDD '17
T2 - 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017
Y2 - 13 August 2017 through 17 August 2017
ER -