KINCL, Tomáš, NOVÁK, Michal, PŘIBIL, Jiří, ŠTRACH, Pavel
In: Social Computing and Social Media: 7th International Conference, SCSM 2015, Held as Part of HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015. Berlin : Springer International Publishing, 2015, s. 158–168. Lecture Notes in Computer Science 9182, Information Systems and Applications, incl. Internet/Web, and HCI, Social Computing and Social Media. ISBN 978-3-319-20366-9
Publication year: 2015

Expressing attitudes and opinions towards various entities (i.e. products, companies, people and events) has become pervasive with the recent proliferation of social media. Monitoring of what customers think is a key task for marketing research and opinion surveys, while measuring customers’ preferences or media monitoring have become a fundamental part of corporate activities. Most experiments on automated sentiment analysis focus on major languages (English, but also Chinese); minor or morphologically rich languages are addressed rather sparsely. Moreover, to improve the performance of machine-learning based classifiers, the models are often complemented with language-dependent components (i.e. sentiment lexicons). Such combined approaches provide a high level of accuracy but are limited to a single language or a single thematic domain.
This paper aims to contribute to this field and introduces an experiment utilizing a language– and domain– independent model for sentiment analysis. The model has been previously tested on multiple corpora, providing a trade-off between generality and the classification performance of the model. In this paper, we suggest a further extension of the model utilizing the surrounding context of the classified documents.