SACAM - A Model for Describing and Classifying Sentiment Analysis Methods

Aleksander Waloszek, Wojciech Waloszek

2017

Abstract

In this paper we introduce SACAM — a model for describing and classifying sentiment analysis (SA) methods. The model focuses on the knowledge used during processing textual opinions. SACAM was designed to create informative descriptions of SA methods (or classes of SA methods) and is strongly integrated with its accompanying graphical notation suited for presenting the descriptions in diagrammatical form. The paper discusses applications of SACAM and shows directions of its further development.

References

  1. Pang, B., Lee, L., 2008. Opinion mining and sentiment analysis. In Foundations and Trends in Information Retrieval, vol. 2, no. 1-2, pp. 1-135, 2008.
  2. Cambria, E., Schuller, B., Xia, Y., Havasi, C., 2013. New Avenues in Opinion Mining and Sentiment Analysis, in IEEE Intelligent Systems 28 (2), pp. 15-21, 2013.
  3. Liu, B., 2012. Sentiment Analysis and Opinion Mining, Morgan & Claypool.
  4. Nasukawa, T., Yi, J., 2003. Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the KCAP-03.
  5. Dave, K., Lawrence, S., Pennock, D. M., 2003. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of International Conference on World Wide Web (WWW2003).
  6. Liu, J., Cao, Y., Lin, C.-Y., Huang, Y., Zhou, M., 2007. Low-quality product review detection in opinion summarization. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL-2007).
  7. McGlohon, M., Glance, N., Reiter, Z., 2010. Star quality: Aggregating reviews to rank products and merchants. In Proceedings of the International Conference on Weblogs and Social Media (ICWSM-2010).
  8. Asur, S., Huberman, B. A., 2010. Predicting the future with social media. In Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.
  9. Bollen, J., Mao, H., Zeng, X-J., 2011. Twitter mood predicts the stock market. In Journal of Computational Science.
  10. Bar-Haim, R., Dinur, E., Feldman, R., Fresko, M., Goldstein, G., 2011. Identifying and Following Expert Investors in Stock Microblogs. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-2011).
  11. Tumasjan, A., Sprenger, T. O., Sandner, P. G., Welpe, I. M., 2010. Predicting elections with twitter: What 140 characters reveal about political sentiment. Im Proceedings of the International Conference on Weblogs and Social Media (ICWSM-2010).
  12. Chen, B., Zhu, L., Kifer, D., Lee, D., 2010. What is an opinion about? Exploring political standpoints using opinion scoring model. In Proceeedings of AAAI Conference on Artificial Intelligence (AAAI-2010).
  13. Feldman, R., 2013. Techniques and Applications for Sentiment Analysis. In Communications of the ACM, vol. 56(4).
  14. Turney, P. D., 2002. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2002).
  15. Penn Treebank Project, 2016. Alphabetical list of partof-speech tags, https://www.ling.upenn.edu/, Accessed May 2016.
  16. Miller, G. A., 1995: WordNet: A Lexical Database for English. In Communications of the ACM Vol. 38, No. 11: 39-41.
  17. Blei, D. M., Ng, A. Y., Jordan, M. I., 2003. Latent dirichlet allocation. In The Journal of Machine Learning Research.
  18. Blair-Goldensohn, S., Hannan, K., McDonald, R., Neylon, T., Reis, G. A., Reynar, J., 2008. Building a sentiment summarizer for local service reviews. In Proceedings of WWW-2008 workshop on NLP in the Information Explosion Era.
  19. Somasundaran, S., Wiebe, J., 2009. Recognizing stances in online debates. In Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP.
  20. Gruber, T. R., 1993. A Translation Approach to Portable Ontologies. In Knowledge Acquisition, 5(2):199-220.
  21. OWL 2, 2012. OWL 2 Web Ontology Language Primer, 2nd Edition, W3C Recommendation.
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Paper Citation


in Harvard Style

Waloszek A. and Waloszek W. (2017). SACAM - A Model for Describing and Classifying Sentiment Analysis Methods . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 196-206. DOI: 10.5220/0006199901960206


in Bibtex Style

@conference{icaart17,
author={Aleksander Waloszek and Wojciech Waloszek},
title={SACAM - A Model for Describing and Classifying Sentiment Analysis Methods},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={196-206},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006199901960206},
isbn={978-989-758-220-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - SACAM - A Model for Describing and Classifying Sentiment Analysis Methods
SN - 978-989-758-220-2
AU - Waloszek A.
AU - Waloszek W.
PY - 2017
SP - 196
EP - 206
DO - 10.5220/0006199901960206