Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme  

作　　者：P.Ramya B.Karthik 

机构地区：[1]Anna University,Chennai,600025,Tamil Nadu,India [2]Sona College of Technology,Salem,636005,Tamilnadu,India

出　　处：《Intelligent Automation & Soft Computing》2023年第5期2379-2391,共13页智能自动化与软计算（英文）

摘　　要：Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach.This paper presented the text document classification that has wide applications in information retrieval,which uses movie review datasets.Here the document indexing based on controlled vocabulary,adjective,word sense disambiguation,generating hierarchical cate-gorization of web pages,spam detection,topic labeling,web search,document summarization,etc.Here the kernel support vector machine learning algorithm helps to classify the text and feature extract is performed by cuckoo search opti-mization.Positive review and negative review of movie dataset is presented to get the better classification accuracy.Experimental results focused with context mining,feature analysis and classification.By comparing with the previous work,proposed work designed to achieve the efficient results.Overall design is per-formed with MATLAB 2020a tool.

关 键 词：Text classification word sense disambiguation kernel support vector machine learning algorithm cuckoo search optimization feature extraction 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]