Aspect-location attention networks for aspect-category sentiment analysis in social media

Pengfei Yu, Wenan Tan, Weinan Niu, Bing Shi

Abstract: As a fine-grained sentiment analysis, aspect-category sentiment classification aims to explore the implicit aspect information in text and analyze its sentiment polarity. When researching review data in social media, this task can often gain insight into the specific needs of users for a certain aspect of products, which is of great significance for commercial companies to improve their products. However, most aspect-level sentiment analysis targets aspect objects that appear directly in the text, which is limited in many scenarios. Furthermore, existing methods for aspect-category sentiment analysis rarely focus on the implicit location of aspect-category information in the context. To this end, the concept of Aspect-Location Attention Networks (ALAN) is proposed to integrate aspect-specific sentiment features for sentiment classification. In ALAN, a novel module is designed to differentially integrate aspect-category information into various locations of the context. The proposed models and their ablation models have been evaluated on three publicly available social review datasets, including two in English and one in Chinese. The experimental results show that ALAN and its variants outperform compared baseline models in terms of accuracy and macro F1-score.