Fine-Grained Opinion Analysis

The MPQA Opinion Corpus contains 535 news articles from a wide variety of news sources manually annotated for opinions and other private states (i.e., beliefs, emotions, sentiments, speculations, etc.). Source: http://www.cs.cornell.edu/home/llee/omsa/omsa.pdf Image Source: https://mpqa.cs.pitt.edu/

With the rise of social media, user-generated content has surged, and hate speech has proliferated. Hate speech targets groups or individuals based on race, religion, gender, region, sexual orientation, or physical traits, expressing malice or inciting harm. Recognized as a growing social issue, it affects 9.41 billion Mandarin Chinese speakers (12% of the global population). However, research on Chinese hate speech detection lags, facing two key challenges. First, existing studies focus on post-level analysis, but post-level classification cannot fully assess models' understanding of hate semantics. Hate speech intensity and directionality depend on associated targets and arguments. In Chinese, flexible word order and lack of segmentation markers (e.g., spaces in English) make fragment-level detection challenging. To address this, we created the first fragment-level Chinese hate speech dataset, annotated with (Target-Argument-Hate Group-Hateful) quadruples. Second, while priorr work provided hate word lists, the lack of interpretive annotations hinders deep semantic analysis. As the only widely used logographic script, Chinese haThe numerous synonyms and near-synonyms, making hate slang diverse and hard to detect. Chinese hate expressions often evade detection through homophones, character splitting/combining , or historical allusions To tackle this, we collected common hate slang from real online forums and built the first annotated Chinese hate slang lexicon.