Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme

Abstract

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 categorization 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 optimization. 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 performed with MATLAB 2020a tool.

A Novel Statistical Theoretical Split Metric for Decision Tree Classification

“…Once all energy levels are computed the feature with minimum value is considered the best attribute for split. In the future works, it is proposed to apply in different applications such as page ranking [13], communication theory [3], , analysing epidemic datasets i.e., COVID-19 [14], image text classification [15], biomedical applications [16] [17], price monitoring [18], sentiment classification [19], text comparison [20] etc.…”

Comparison of Pre-trained vs Custom-trained Word Embedding Models for Word Sense Disambiguation

“…However, the majority of work on WSD is for English and other well-reputed languages, and there have been few attempts for Urdu. In recent years, some researchers have worked on WSD, including (Mir et al, 2023;Zhang, et al 2023;Ramya and Karthik, 2023).…”