학술논문

AI Driven Sentiment Analysis for Social Media Data
Document Type
Conference
Source
2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) Computing, Communication, and Intelligent Systems (ICCCIS), 2023 International Conference on. :1201-1206 Nov, 2023
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Robotics and Control Systems
Surveys
Training
Text mining
Sentiment analysis
Machine learning algorithms
Social networking (online)
Videos
Sentiment classifification
Feature selection
Emotion detection
Transfer learning
Building resources
Language
Abstract
Sentiment analysis (SA) is a subject of ongoing text mining research. The subjectivity, emotions, and viewpoints of a text are handled algorithmically by sentiment analysis. This survey research examines the most recent development in this field in great detail. This paper examines and provides a quick overview of various recently suggested algorithm enhancements. Based on their contributions to the various sentiment analysis approaches, these articles are categorized into various categories. Sentiment analysis may be utilised to extract information related to thoughts and feelings from human-generated textual data. Users have posted a significant quantity of unprocessed material to social networking sites in the shape of speech, videos, photographs, and audio. Global catastrophes including heatwaves, earthquakes, cyclones, floods and bushfires are having an extraordinary impact on social media users' life. They frequently write how pessimistic they find the catastrophic scenarios at their target area to be. Politicians and strategic planners must prioritise comprehending location-specific attitudes towards crisis situations. Data may be turned into valuable knowledge by using sentiment analysis. The primary goal of the questionnaire is to provide a concise, almost complete picture of Sentiment Analysis methods and associated topics. The comprehensive categories of several recently published papers and the explanation of the present state of research in sentiment evaluation as well as related fields are the key contributions made by this study.