Significance and Predictive Classification Algorithms in Sentiment Analysis  

作　　者：V Uma V Ganesh 

机构地区：[1]Department of Computer and Information Science,Annamalai University,Cuddalore 608002,Tamil Nadu,India [2]Department of Computer Science,Government Arts College(Autonomous),Kumbakonam 612002,Tamil Nadu,India

出　　处：《Journal of Harbin Institute of Technology(New Series)》2024年第6期37-48,共12页哈尔滨工业大学学报（英文版）

摘　　要：In recent times, an abrupt upswing has emerged within the data mining domain, particularly within the sphere of sentiment analysis. Encompassing diverse dimensions such as sentiment extraction, subjectivity categorization, opinion summarization, and spam detection, sentiment analysis, also acknowledged as Opinton Mining(OM), undertakes a mathematical exploration into the perspectives, emotions, evaluations, and conduct of individuals toward entities, encompassing products, services, individuals, and events. The advent of web technology tools empowers users to liberally articulate their opinions across varied online platforms, culminating in the generation of a substantial corpus of invaluable yet unstructured data. This exposition scrutinizes the importance of harnessing this data, delving into the intricate process of refining and metamorphosing it for subsequent operations like classification and aspect-oriented sentiment analysis. The crux of the discourse centers on a thorough scrutiny of four data mining algorithms deployed for prognostication and categorization, providing insights into their efficacy within the domain of sentiment analysis. This investigation transcends into pragmatic applications, challenges encountered in the field, and potential trajectories ahead, culminating in a nuanced comprehension of the dynamic panorama of sentiment analysis and its ramifications.

关 键 词：data mining OM CLASSIFICATION 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]