Sentiment analysis in czech social media using supervised machine learning
Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (2013)
Research topics:
Sentiment AnalysisAbstract
This article provides an in-depth research of machine learning methods for sentiment analysis of Czech social media. Whereas in English, Chinese, or Spanish this field has a long history and evaluation datasets for various domains are widely available, in case of Czech language there has not yet been any systematical research conducted. We tackle this issue and establish a common ground for further research by providing a large humanannotated Czech social media corpus. Furthermore, we evaluate state-of-the-art supervised machine learning methods for sentiment analysis. We explore different pre-processing techniques and employ various features and classifiers. Moreover, in addition to our newly created social media dataset, we also report results on other widely popular domains, such as movie and product reviews. We believe that this article will not only extend the current sentiment analysis research to another family of languages, but will also encourage competition which potentially leads to the production of high-end commercial solutions.
Authors
BibTex
@inproceedings{habernal2013sentiment, title = {Sentiment analysis in czech social media using supervised machine learning}, author = {Habernal, Ivan and Pt{\'a}cek, Tom{\'a}{\v{s}} and Steinberger, Josef}, booktitle = {Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis}, pages = {65--74}, year = {2013}, abstract = {This article provides an in-depth research of machine learning methods for sentiment analysis of Czech social media. Whereas in English, Chinese, or Spanish this field has a long history and evaluation datasets for various domains are widely available, in case of Czech language there has not yet been any systematical research conducted. We tackle this issue and establish a common ground for further research by providing a large humanannotated Czech social media corpus. Furthermore, we evaluate state-of-the-art supervised machine learning methods for sentiment analysis. We explore different pre-processing techniques and employ various features and classifiers. Moreover, in addition to our newly created social media dataset, we also report results on other widely popular domains, such as movie and product reviews. We believe that this article will not only extend the current sentiment analysis research to another family of languages, but will also encourage competition which potentially leads to the production of high-end commercial solutions.} }
