A Study of Human Emotion Analysis Based on Social Media
- DOI
- 10.2991/978-2-38476-062-6_23How to use a DOI?
- Keywords
- sentiment analysis; social media; sentiment lexicon; deep learning
- Abstract
Social media, a new platform for online interaction, has dramatically changed the way people communicate, interaction and think, while facilitating an explosion of user-generated information. Large number of user-generated social media texts have become one of the most representative data sources of big data in recent years, and mining and analyzing these user-generated information has far-reaching implications for social development. Social media text sentiment analysis, as an information processing technique for analyzing, processing, generalizing and reasoning about subjective texts with emotional overtones, has received widespread attention from academia and industry in recent years, and has been widely applied in many fields of the Internet. Traditional research work on text sentiment analysis focuses on analyzing sentiment from text, but ignores the individual differences in users’ expression of sentiment, thus affecting the quality of the analysis results.
- Copyright
- © 2023 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 - Hanzun Zhang PY - 2023 DA - 2023/07/11 TI - A Study of Human Emotion Analysis Based on Social Media BT - Proceedings of the 2023 2nd International Conference on Social Sciences and Humanities and Arts (SSHA 2023) PB - Atlantis Press SP - 174 EP - 180 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-062-6_23 DO - 10.2991/978-2-38476-062-6_23 ID - Zhang2023 ER -