Aspect Sentiment Model for Micro Reviews
This addresses the problem of analyzing sentiment in short user reviews for applications like social media or e-commerce, though it is incremental as it builds on existing aspect sentiment models.
The paper tackles aspect-based sentiment analysis for short micro reviews by proposing MicroASM, which models reviews as sentiment-aspect word pairs and clusters them, resulting in improved performance over state-of-the-art models on tasks like aspect term extraction and sentiment classification.
This paper aims at an aspect sentiment model for aspect-based sentiment analysis (ABSA) focused on micro reviews. This task is important in order to understand short reviews majority of the users write, while existing topic models are targeted for expert-level long reviews with sufficient co-occurrence patterns to observe. Current methods on aggregating micro reviews using metadata information may not be effective as well due to metadata absence, topical heterogeneity, and cold start problems. To this end, we propose a model called Micro Aspect Sentiment Model (MicroASM). MicroASM is based on the observation that short reviews 1) are viewed with sentiment-aspect word pairs as building blocks of information, and 2) can be clustered into larger reviews. When compared to the current state-of-the-art aspect sentiment models, experiments show that our model provides better performance on aspect-level tasks such as aspect term extraction and document-level tasks such as sentiment classification.