Towards Metadata Analysis on Opinionated Content in Tweets
Anderson Almeida Firmino, Cláudio de Souza Baptista, André Luiz Firmino Alves, Davi Oliveira Serrano de Andrade, Hugo Feitosa de Figueirêdo, Geraldo Braz Filho, Anselmo Cardoso de Paiva
2016
Abstract
Recently, much research has been done in the area of sentiment analysis of microtexts, specially using tweets. In most studies, the sentiment polarity detection methods are solely based on textual information. The detection of opinionated content in texts is not a simple task, and even less simple in the context of social media. Furthermore, processing microtexts using just natural language techniques may lead to unsatisfactory results. There is a lack of works which link other properties of the tweets (metadata), such as retweets and likes, and the their opinion (i.e., the presence of sentiments). Using tweets collected during the 2013 FIFA Confederations Cup, which occurred in Brazil, this work proposes an analysis of metadata properties on tweets, in order to verify which of these properties have more impact on their opinionatedness. The results indicate that the properties “presence of links” and “retweets” are the most significant with respect to the opinionatedness of a tweet.
References
- Alves, A. L. F, Baptista, C., Firmino, A., Oliveira, G., Figueirêdo, H., 2014. Temporal Analysis of Sentiment in Tweets: a Case Study with FIFA Confederations Cup in Brazil. Database and Expert Systems Applications: 25th International Conference, DEXA, Munich, Germany, September 1-4. Proceedings, Part 1.
- Alves, A. L. F., 2014. An Approach for SpatioTemporal Sentiment Analysis in Microtexts (in Portuguese). Master Thesis. Federal University of Campina Grande, Brazil.
- Cambria E,, Speer R., Havasi C., and Hussain A., 2010. SenticNet: A Publicly Available Semantic Resource for Opinion Mining. In AAAI Fall Fymposium: Commonsense Knowledge (Vol. 10, p. 02).
- Cambria, E.; Schuller, B.; Liu, B.; Wang, H.; Havasi, C., 2013a. Knowledge-based approaches to concept-level sentiment analysis. IEEE Intelligent Systems, v. 28, n. 2, p. 12-14.
- Cambria, E.; Schuller, B.; Xia, Y.; Havasi, C., 2013b. New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems, v. 28, n. 2, p. 15-21.
- Cambria, E.; Song, Y.; Wang, H.; Howard, N., 2014. Semantic multidimensional scaling for open-domain sentiment analysis. Intelligent Systems, IEEE, v. 29, n. 2, p. 44-51.
- Hogenboom, A.; Frasincar, F.; de Jong, F.; Kaymak, U., 2015. Using rhetorical structure in sentiment analysis. Communications of the ACM, v. 58, n. 7, p. 69-77.
- Hosmer Jr., D. W., Lemeshow, S., & Sturdivant, R. X. 2013. Applied Logistic Regression. Hoboken, NJ, USA: John Wiley & Sons, Inc.
- Liu, B., 2012. Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1):1-167.
- Liu, S. M.; Chen, J. H.., 2015. A multi-label classification based approach for sentiment classification. Expert Systems with Applications, v. 42, n. 3, p. 1083-1093.
- Pak, A., Paroubek, P., 2010. Twitter as a Corpus for Sentiment Analysis and Opinion Mining. Proceedings of the Seventh conference on International Language Resources and Evaluation LREC'10 pp. 1320-1326.
- Pfitzner, R., Garas, A., Schweitzer, F., 2012. Emotional Divergence Influences Information Spreading in Twitter. Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media.
- Poria, S.; Gelbukh, A.; Hussain, A.; Das, D., 2013. Bandyopadhuay, S. Enhanced SenticNet with affective labels for concept-based opinion mining. IEEE Intelligent Systems, v. 28, n. 2, p. 31-38.
- Rosas, V. P.; Mihalcea, R.; Morency, L.P., 2013. Multimodal sentiment analysis of Spanish online videos. IEEE Intelligent Systems, v. 28, n. 3, p. 38-45.
- Sharma, A. and Dey S., 2012. A comparative study of feature selection and machine learning techniques for sentiment analysis. In Proceedings of the 2012 ACM Research in Applied Computation Symposium on - RACS 7812, page 1, New York, USA. ACM Press.
- Stieglitz, S., Dang-Xuan, L., 2012. Political Communication and Influence through Microblogging - An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior. Proceedings of the 45th Hawaii International Conference on System Sciences.
- Suh, B., Hong, L., Pirolli, P., and Chi, E., 2010. Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network. IEEE International Conference on Social Computing / IEEE International Conference on Privacy, Security, Risk and Trust.
- Harris JK, Mart A, Moreland-Russell S, Caburnay C., 2015. Diabetes Topics Associated With Engagement on Twitter. Prev Chronic Dis.
- Meier, F., Elsweiler, D., Wilson, M., 2014. More than Liking and Bookmarking? Towards Understanding Twitter Favouriting Behaviour. Proceedings of the 8th International AAAI Conference on Weblogs and Social Media.
- Tsai, A. C. R.; Wu, C. E.; Tsai, R. T. H.; Hsu, J. Y. J., 2013. Building a concept-level sentiment dictionary based on commonsense knowledge. IEEE Intelligent Systems, v. 28, n. 2, p. 22-30.
- Tsytsarau, M. and Palpanas, T., 2012. Survey on mining subjective data on the web. Data Min. Knowl. Discov., 24(3):478-514.
- Xia, R.; Zong, C.; Hu, X.; Cambria, E., 2013. Feature ensemble plus sample selection: domain adaptation for sentiment classification. Intelligent Systems, IEEE, v. 28, n. 3, p. 10-18.
- Weichselbraun, A.; Gindl, S.; Scharl, A., 2013. Extracting and grounding context-aware sentiment lexicons. IEEE Intelligent Systems, v. 28, n. 2, p. 39-46.
- Wollmer, M.; Weninger, F.; Knaup, T.; Schuller, B.; Sun, C.; Sagae, K.; Morency, L.P., 2013. Youtube movie reviews: Sentiment analysis in an audio-visual context. Intelligent Systems, IEEE, v. 28, n. 3, p. 46-53.
Paper Citation
in Harvard Style
Firmino A., Baptista C., Alves A., Andrade D., Figueirêdo H., Filho G. and de Paiva A. (2016). Towards Metadata Analysis on Opinionated Content in Tweets . In Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-187-8, pages 314-320. DOI: 10.5220/0005890803140320
in Bibtex Style
@conference{iceis16,
author={Anderson Almeida Firmino and Cláudio de Souza Baptista and André Luiz Firmino Alves and Davi Oliveira Serrano de Andrade and Hugo Feitosa de Figueirêdo and Geraldo Braz Filho and Anselmo Cardoso de Paiva},
title={Towards Metadata Analysis on Opinionated Content in Tweets},
booktitle={Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2016},
pages={314-320},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005890803140320},
isbn={978-989-758-187-8},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Towards Metadata Analysis on Opinionated Content in Tweets
SN - 978-989-758-187-8
AU - Firmino A.
AU - Baptista C.
AU - Alves A.
AU - Andrade D.
AU - Figueirêdo H.
AU - Filho G.
AU - de Paiva A.
PY - 2016
SP - 314
EP - 320
DO - 10.5220/0005890803140320