Research on Sentiment Analysis in Network Public Opinion – A Case Study of Song Plagiarism
- DOI
- 10.2991/978-94-6463-044-2_110How to use a DOI?
- Keywords
- Song Online Review; LSTM; Word2vec; Sentiment Analysis
- Abstract
In the Internet age, the online music platform is not only a platform for people to listen to music, but also a platform for comments, exchanges, and sentiments. In addition to reflecting users’ opinions, song online review can also be used as an important basis for sentiment analysis. At the same time, it can also be used as an important reference indicator for the recommendation system. This paper takes the song “Out of the Mountain” in NetEase Cloud Music as an example, and uses the LSTM neural network model to perform sentiment analysis on the song network comment data. Because of its official release and popular social media for a period of time, a “plagiarism” scandal broke out. Therefore, two extreme emotions appeared before and after his comment. This increases the complexity of the research object, and at the same time has certain reference significance and value for the analysis of emotional changes caused by the impact of emergencies.
- Copyright
- © 2022 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Yao Li AU - Sihui Li AU - Jingjing Jiang AU - Huimei Wei PY - 2022 DA - 2022/12/27 TI - Research on Sentiment Analysis in Network Public Opinion – A Case Study of Song Plagiarism BT - Proceedings of the 2022 3rd International Conference on Modern Education and Information Management (ICMEIM 2022) PB - Atlantis Press SP - 886 EP - 893 SN - 2667-128X UR - https://doi.org/10.2991/978-94-6463-044-2_110 DO - 10.2991/978-94-6463-044-2_110 ID - Li2022 ER -