Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review.
Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review.
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DOI：
10.1007/s42979-021-00958-1
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Kanaga EGM
中科院分区：
文献类型：
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作者：
Babu NV;Kanaga EGM
情绪分析是当今的一种新兴趋势，旨在了解人们在日常生活中多种情况下的情绪。社交媒体数据将用于整个过程，即分析和分类过程，它由文本数据和表情符号、表情符号等组成。在先前的研究中，利用二元和三元分类进行了许多实验，而多类分类提供了更精确和精确的分类。在多类分类中，数据将根据极性分为多个子类。机器学习和深度学习技术将用于分类过程。利用社交媒体，可以监控或分析情绪水平。本文回顾了利用各种人工智能技术对社交媒体数据进行忧虑或沮丧检测的情绪分析。在调查中，光学探讨了由文本、表情符号和表情符号组成的社交媒体数据被用于利用各种人工智能技术进行情感识别。深度学习算法的多类分类在情感分析中显示出更高的精度值。
Sentiment analysis is an emerging trend nowadays to understand people’s sentiments in multiple situations in their quotidian life. Social media data would be utilized for the entire process ie the analysis and classification processes and it consists of text data and emoticons, emojis, etc. Many experiments were conducted in the antecedent studies utilizing Binary and Ternary Classification whereas Multi-class Classification gives more precise and precise Classification. In Multi-class Classification, the data would be divided into multiple sub-classes predicated on the polarities. Machine Learning and Deep Learning Techniques would be utilized for the classification process. Utilizing Social media, sentiment levels can be monitored or analysed. This paper shows a review of the sentiment analysis on Social media data for apprehensiveness or dejection detection utilizing various artificial intelligence techniques. In the survey, it was optically canvassed that social media data which consists of texts,emoticons and emojis were utilized for the sentiment identification utilizing various artificial intelligence techniques. Multi Class Classification with Deep Learning Algorithm shows higher precision value during the sentiment analysis.