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
