Sentiment classification of time-sync comments: A semi-supervised hierarchical deep learning method

Abstract

Time-sync comment (TSC) has emerged as a new type of textual comment for real-time user interactions on online video platforms. The sentiment classification of TSCs provides considerable potential for platforms to optimize operation strategies but inevitably faces great challenges due to the TSCs’ often uninformative and informal text. Considering the contextual dependency among TSCs posted within the same video clip, this study posits that contextual TSCs may benefit the sentiment classification of a target TSC. To address the challenges of leveraging contextual TSCs, such as their semantic representation and fusion, we propose a semi-supervised hierarchical deep learning method for the sentiment classification of TSCs. We design a hierarchical architecture to capture the semantics of TSCs at the word, comment, and context levels. Considering the varying importance of words and comments, we also design attention mechanisms to focus on important sentiment information and fuse semantic representations. Empirical evaluation shows that the proposed method outperforms benchmarked sentiment classification methods. This study advances our knowledge of contextual information indicative of TSC sentiment, and contributes to improving the service operation of online video platforms.