Sentiment Analysis

Sentiment Analysis is the detection of attitudes. The basic task is to automatically decide whether a piece of text (e.g. a review, a tweet, a blog post, or a general document) is positive or negative. Also the attitude’s polarity as well as the target, source, or complex types are detected.

In our research, we focus on sentiment analysis in the Czech web environment, with a special attention to social media. In our pilot paper, we created a large annotated corpus from the top 10 Czech facebook brands and achieved the recognition accuracy about 70% (see the paper Sentiment Analysis in Czech Social Media Using Supervised Machine Learning. The corpus is freely available for further research. Since NLP in Czech suffers from its large vocabulary and very rich flection in general, we furhter improved our methods by incorporating semi-supervised features based on statistical distributional semantics Semantic Spaces for Sentiment Analysis

Our experiments in both Czech and English movie review domains achieved the state-of-the-art performance on a widely used datased in the sentiment analysis task (about 92% accuracy). For details, please refer to our paper Unsupervised Improving of Sentiment Analysis Using Global Target Context.

Other datasets regarding sentiment analysis and stance detection are available here: https://corpora.kiv.zcu.cz/sentiment/

See also our fact checking section: https://corpora.kiv.zcu.cz/fact-checking

Publications

Stance and Sentiment in Czech
Tomáš Hercig Peter Krejzl Pavel Král
Computación y Sistemas (2018)
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Sentiment Analysis
UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets
Pavel Přibáň and Tomáš Hercig Ladislav Lenc
Proceedings of The 12th International Workshop on Semantic Evaluation (2018)
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Sentiment Analysis
UWB at SemEval-2018 Task 3: Irony detection in English tweets
Tomáš Hercig
Proceedings of The 12th International Workshop on Semantic Evaluation (2018)
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Sentiment Analysis
Detecting Stance in Czech News Commentaries
Tomáš Hercig Peter Krejzl Josef Steinberger and Ladislav Lenc
Proceedings of the 17th ITAT: Slovenskočeský NLP workshop (SloNLP 2017) (2017)
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Sentiment Analysis
UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis
Tomáš Hercig Tomáš Brychcín and Lukáš Svoboda Michal Konkol
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) (2016)
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Sentiment Analysis
Neural Networks for Sentiment Analysis in Czech
Ladislav Lenc and Tomáš Hercig
Proceedings of the 16th ITAT: Slovenskočeský NLP workshop (SloNLP 2016) (2016)
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Sentiment Analysis
Sarcasm Detection on Czech and English Twitter
Tomáš Hercig Ivan Habernal
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers (2014)
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Sentiment Analysis
UWB: Machine Learning Approach to Aspect-Based Sentiment Analysis
Tomáš Brychcín and Michal Konkol Josef Steinberger
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) (2014)
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Semantic Analysis | Sentiment Analysis | Named Entity Recognition
Supervised sentiment analysis in Czech social media
Ivan Habernal Tomáš Hercig Josef Steinberger
Information Processing & Management (2014)
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Sentiment Analysis
Aspect-Level Sentiment Analysis in Czech
Josef Steinberger and Tomáš Brychcín and Michal Konkol
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (2014)
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Sentiment Analysis
Sentiment analysis in czech social media using supervised machine learning
Ivan Habernal Tomáš Hercig Josef Steinberger
Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (2013)
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Sentiment Analysis
Unsupervised Improving of Sentiment Analysis Using Global Target Context
Tomáš Brychcín and Ivan Habernal
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013 (2013)
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Sentiment Analysis
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