Keywords
How to Cite
Abstract
In the age of digital communication, an abundance of textual data is generated daily through various channels, such as social media, emails, and chat applications. This wealth of textual information has opened up new avenues for understanding and evaluating the emotional states of individuals. Text analysis, a powerful tool in natural language processing (NLP), has emerged as a valuable method for gaining insights into the emotions, sentiments, and psychological well-being of people. This article explores the method of text analysis and its application in evaluating the emotional state of individuals through textual data.
References
M. Heidari, J. H. J. Jones, and O. Uzuner, “An empirical studyof machine learning algorithms for social media bot detection,”in IEEE 2021 International IOT, Electronics and MechatronicsConference, IEMTRONICS 2021, 2021.
M. Heidari and S. Rafatirad, “Using transfer learning approachto implement convolutional neural network model to recommendairline tickets by using online reviews,” in 2020 15th Interna-tional Workshop on Semantic and Social Media Adaptation andPersonalization (SMA), pp. 1–6, 2020
G. Langroudi, A. Jourdanous, and L. Li, “Music emotion capture:sonifying emotions in eeg data,” in Symposium on EmotionModeling and Detection in Social Media and Online Interaction,vol. 5, 2018
Samira Zad, Maryam Heidari, James H Jr Jones and Ozlem Uzuner, “Emotion Detection of Textual Data: An Interdisciplinary Survey” in May 2021 Conference: 2021 IEEE World AI IoT Congress (AIIoT)
Renzo Carli, Rosa Maria Paniccia, Fiammetta Giovagnoli, Agostino Carbone, Fiorella Bucci, 2 Emotional Textual Analysis
Edgar Gutierrez,Waldemar Karwowski,Krzysztof Fiok,Mohammad Reza Davahli,Tameika Liciaga, andTareq Ahram, “Analysis of Human Behavior by Mining Textual Data: Current Research Topics and Analytical Techniques” Symmetry 2021, 13, 1276. https://doi.org/ 10.3390/sym13071276
This work is licensed under a Creative Commons Attribution 4.0 International License.