Aspect Sentiment Model for Micro Reviews

TL;DR

This paper introduces MicroASM, a novel aspect sentiment model tailored for micro reviews, effectively capturing sentiment and aspects in short texts where traditional models struggle due to limited co-occurrence data.

Contribution

MicroASM is a new model that leverages sentiment-aspect word pairs and review clustering to improve aspect-based sentiment analysis on micro reviews.

Findings

MicroASM outperforms existing models in aspect term extraction.

MicroASM achieves higher accuracy in sentiment classification.

The model effectively handles short review data with limited co-occurrence patterns.

Abstract

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.