[2G6-GS-6] Language media processing:

[2G6-GS-6-02] An Extension and a Validation of Multi-label Classification Method Using Deep Language Model for Japanese Aspect-based Sentiment Analysis

〇Haruki Ajioka¹, Makoto Okada², Naoki Mori² (1. Osaka Prefecture University, 2. Osaka Metropolitan University)

Keywords:Aspect-based sentiment analysis, BERT, Ensemble learning, Deep learning

Nowadays, there is a large amount of text data such as SNS posts and reviews on the Internet. Such text data contain a lot of useful information such as evaluations and impressions of various objects. However, in order to make use of such information, it is essential to label them appropriately and to automate the labeling process. Sentiment analysis is a task in the field of natural language processing that classifies text into positive or negative polarity, and in particular aspect-based sentiment analysis extracts multiple objects in a text and classifies their polarity, which is effective for automating label assignment in that it can extract detailed information. In this study, based on Mpm+T, a model proposed by Kusumoto for Japanese aspect-based sentiment analysis, we propose an extension of Mpm+T to improve Mpm+T to handle data in which a positive and a negative label are assigned simultaneously to a single target, which is not handled by Mpm+T. We also verify the effectiveness of the proposed method through experiments.