Sentiment Analysis for Title-Sentence Sequences of News Articles

Abstract

We propose a method to identify emotion labels of titles and sentences of news articles using a model combining BERT and BiLSTM-CRF as a problem of sequence labeling. First, we constructed a dataset with emotion labels ("positive," "negative," or "neutral") annotated for titles and sentences of news articles, and then evaluated its effectiveness of the model using the dataset. Furthermore, as an application example, we demonstrate a task of manually classifying articles written about a certain keyword into positive, negative, or neutral. We confirmed that the classification work could be completed in a shorter time than when these emphasis was not applied when the colors of titles and sentences were emphasized according to the estimated emotion labels.