A First Approach to Mining Opinions as Multisets through Argumentation
Web 2.0 technologies have resulted in an exponential growth of text-based opinions coming from different sources (such as online news media, microblogging platforms, social networks, online review systems, etc.). The assessment of such opinions has gained considerable interest within several research communities in Computer Science, particularly in the context of modelling decision making processes. In this context, the scientific study of emotions in opinions associated with a given topic has become particularly relevant. Some approaches for assessing emotions in text-based opinions have been developed, resulting in promising software tools for sentiment analysis. In spite of the existence of such tools, assessing and contrasting text-based opinions is indeed a difficult task. On the one hand, complex opinions are built in many cases bottom up, emerging by aggregation from individual opinions posted online. On the other hand, contradictory and potentially inconsistent information might arise when contrasting such complex opinions. This article introduces an argument-based framework which allows to mine text-based opinions based on incrementally generated topics along with partially-ordered features, which provide a multidimensional comparison criterion. Given a topic, we will model an atomic opinion supporting it as a multiset (or bag) of terms. Atomic opinions can be aggregated, and related to alternative opinions, based on expanded topics. As a result, we will be able to obtain an “opinion analysis tree”, rooted in the first original topic.
KeywordsMining Opinion Sentiment Analysis Generic User Argumentation Theory Argumentation Framework
Unable to display preview. Download preview PDF.
- 2.Martineau, J.: Identifying and Isolating Text Classification Signals from Domain and Genre Noise for Sentiment Analysis. PhD thesis, University of Maryland, Baltimore County, USA (2011)Google Scholar
- 4.Chesñevar, C.I., Maguitman, A.G., Estevez, E., Brena, R.F.: Integrating argumentation technologies and context-based search for intelligent processing of citizens’ opinion in social media. In: Ferriero, D., Pardo, T.A., Qian, H. (eds.) ICEGOV, pp. 166–170. ACM (2012)Google Scholar
- 5.Mizumoto, K., Yanagimoto, H., Yoshioka, M.: Sentiment analysis of stock market news with semi-supervised learning. In: Miao, H., Lee, R.Y., Zeng, H., Baik, J. (eds.) ACIS-ICIS, pp. 325–328. IEEE (2012)Google Scholar
- 6.Besnard, P., Hunter, A.: The Elements of Argumentation. The MIT Press, London (2008)Google Scholar
- 7.Rahwan, I., Simari, G.: Argumentation in Artificial Intelligence. Springer (2009)Google Scholar
- 8.Grosse, K., Chesñevar, C.I., Maguitman, A.G.: An argument-based approach to mining opinions from twitter. In: Ossowski, S., Toni, F., Vouros, G.A. (eds.) AT. CEUR Workshop Proceedings, vol. 918, pp. 408–422. CEUR-WS.org (2012)Google Scholar
- 11.Torroni, P., Toni, F.: Bottom up argumentation. In: Prof. of First Intl. Workshop on Theoretical and Formal Argumentation (TAFA). IJCAI 2011, Barcelona, Spain (2011)Google Scholar
- 12.Leite, J., Martins, J.: Social abstract argumentation. In: Walsh, T. (ed.) IJCAI, IJCAI/AAAI, pp. 2287–2292 (2011)Google Scholar
- 15.Modgil, S., Toni, F., Bex, F., Bratko, I., Chesñevar, C., Dvořák, W., Falappa, M.A., Gaggl, S.A., García, A.J., Gonzalez, M.P., Gordon, T.F., Leite, J., Mozina, M., Reed, C., Simari, G.R., Szeider, S., Torroni, P., Woltran, S.: The Added Value of Argumentation: Examples and Challenges. In: Handbook of Agreement Technologies, pp. 357–404. Springer (2013)Google Scholar