That's So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets
William Yang Wang, Diyi Yang · 2015
We propose a novel data augmentation approach to enhance computational behavioral analysis using social media text.In particular, we collect a Twitter corpus of the descriptions of annoying behaviors using the #petpeeve hashtags.In the qualitative analysis, we study the language use in these tweets, with a special focus on the fine-grained categories and the geographic variation of the language.In quantitative analysis, we show that lexical and syntactic features are useful for automatic categorization of annoying behaviors, and frame-semantic features further boost the performance; that leveraging large lexical embeddings to create additional training instances significantly improves the lexical model; and incorporating frame-semantic embedding achieves the best overall performance.* We understand that many people find long titles annoying, so we intentionally use a very long one to help people understand what "pet peeve" means.