A Model for Topic Classification and Extraction of Sentimental Expression using a Lexical Semantic Network

JiEun Park, JuSang Lee, JoonChoul Shin, ChoelYoung Ock

http://doi.org/10.5626/JOK.2023.50.8.700

The majority of the previous sentiment analysis studies classified a single sentence or document into only a single sentiment. However, more than one sentiment can exist in one sentence. In this paper, we propose a method that extracts sentimental expression for word units. The structure of the proposed model is a UBERT model that uses morphologically analyzed sentences as input and adds layers to predict topic classification and sentimental expression. The proposed model uses topic feature of a sentence predicted by topic dictionary. The topic dictionary is built at the beginning of machine learning. The learning module collects topic words from a training corpus and expands them using the lexical semantic network. The evaluation is performed with the word unit F1-Score. The proposed model achieves an F1-Score of 58.19%, an improvement of 0.97% point over the baseline.