Department of Business Administration, National Formosa University, Yunlin 632301, Taiwan.
Department of Information Management, Tamkang University, New Taipei City 251301, Taiwan.
Sensors (Basel). 2022 Jul 13;22(14):5223. doi: 10.3390/s22145223.
Sentiment analysis is one of the fields of affective computing, which detects and evaluates people's psychological states and sentiments through text analysis. It is an important application of text mining technology and is widely used to analyze comments. Bullet screen videos have become a popular way for people to interact and communicate while watching online videos. Existing studies have focused on the form, content, and function of bullet screen comments, but few have examined bullet screen comments using natural language processing. Bullet screen comments are short text messages of different lengths and ambiguous emotional information, which makes it extremely challenging in natural language processing. Hence, it is important to understand how we can use the characteristics of bullet screen comments and sentiment analysis to understand the sentiments expressed and trends in bullet screen comments. This study poses the following research question: how can one analyze the sentiments ex-pressed in bullet screen comments accurately and effectively? This study mainly proposes an ERNIE-BiLSTM approach for sentiment analysis on bullet screen comments, which provides effective and innovative thinking for the sentiment analysis of bullet screen comments. The experimental results show that the ERNIE-BiLSTM approach has a higher accuracy rate, precision rate, recall rate, and F1-score than other methods.
情感分析是情感计算的一个领域，它通过文本分析来检测和评估人们的心理状态和情感。它是文本挖掘技术的一个重要应用，广泛用于分析评论。弹幕视频已成为人们在观看在线视频时进行互动和交流的一种流行方式。现有的研究主要集中在弹幕评论的形式、内容和功能上，但很少有使用自然语言处理来研究弹幕评论。弹幕评论是长短不一、情感信息模糊的短文本消息，这使得在自然语言处理中极具挑战性。因此，了解如何利用弹幕评论的特点和情感分析来理解弹幕评论中的情感表达和趋势非常重要。本研究提出了以下研究问题：如何准确有效地分析弹幕评论中的情感表达？本研究主要提出了一种基于 ERNIE-BiLSTM 的弹幕评论情感分析方法，为弹幕评论的情感分析提供了有效的创新思路。实验结果表明，ERNIE-BiLSTM 方法在准确率、精确率、召回率和 F1 分数方面均优于其他方法。
