Research on the Model of Lyric Emotion Algorithm

Abstract

With the rapid development of Internet technology, various social networking platforms, especially mobile social networking platforms, continue to increase, resulting in a large amount of public opinion information. Internet public opinion has a clear emotional orientation, and its emotional orientation is very easy to spread and be infected, and even affect the development of the event. Aiming at the characteristics of lyric information rich and which are easy to change with time, the lyric theme analysis model and the lyric emotion evolution model are proposed. The LDA model is used to extract the topic from the lyric text in a period of time, and the sensational heat value is calculated according to the forwarding amount and the number of comments, and the lyrical theme with the highest heat is obtained. The relative entropy between sub-topics in the adjacent time slice of a specific hot topic is calculated, and the degree of association between the topics in the adjacent time slice is determined, thereby analyzing whether there is a split of the sub-topic and a new topic. Then the evaluation object is extracted, combined with the joint deep neural network model to judge the emotions of each evaluation object in different time, and the emotional evolution of the hot topic is analyzed from multiple dimensions. Finally, an example analysis of the network public opinion information from June to July 2018 is carried out to verify the validity of the above model. The model effectively solves the problems of immature emotion analysis model and low accuracy of emotion classification in the current public opinion analysis.

Incorporating structural topic modeling into short text analysis

“…Machine learning methods applied in lyric studies usually aim to conduct sentiment analysis (Xia, Gu & Lu 2019) or lyric generation tasks (Wang & Zhao 2019). While STM has been widely adopted to detect topics in education research, social movements, or management discourse (Lindstedt 2019;Chen et al 2020;Aranda et al 2021), how to apply linguistic insights to distinguishing lyric topics from different singers is rarely approached.…”