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• W2967231844 startingPage "1950033" @default.
• W2967231844 abstract "One can either use machine learning techniques or lexicons to undertake sentiment analysis. Machine learning techniques include text classification algorithms like SVM, naive Bayes, decision tree or logistic regression, whereas lexicon-based sentiment analysis uses either general or domain-based lexicons. In this paper, we investigate the effectiveness of domain lexicons vis-à-vis general lexicon, wherein we have performed aspect-level sentiment analysis on data from three different domains, viz. car, guitar and book. While it is intuitive that domain lexicons will always perform better than general lexicons, the actual performance however may depend on the richness of the concerned domain lexicon as well as the text analysed. We used the general lexicon SentiWordNet and the corresponding domain lexicons in the aforesaid domains to compare their relative performances. The results indicate that domain lexicon used along with general lexicon performs better as compared to general lexicon or domain lexicon, when used alone. They also suggest that the performance of domain lexicons depends on the text content; and also on whether the language involves technical or non-technical words in the concerned domain. This paper makes a case for development of domain lexicons across various domains for improved performance, while gathering that they might not always perform better. It further highlights that the importance of general lexicons cannot be underestimated — the best results for aspect-level sentiment analysis are obtained, as per this paper, when both the domain and general lexicons are used side by side." @default.
• W2967231844 created "2019-08-22" @default.
• W2967231844 creator A5012821808 @default.
• W2967231844 creator A5012892639 @default.
• W2967231844 date "2019-09-01" @default.
• W2967231844 modified "2023-09-26" @default.
• W2967231844 title "Effectiveness of Domain-Based Lexicons vis-à-vis General Lexicon for Aspect-Level Sentiment Analysis: A Comparative Analysis" @default.
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• W2967231844 doi "https://doi.org/10.1142/s0219649219500333" @default.
• W2967231844 hasPublicationYear "2019" @default.
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