文章基本信息

标题：Exerting 2D-Space of Sentiment Lexicons with Machine Learning Techniques: A Hybrid Approach for Sentiment Analysis
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作者：Muhammad Yaseen Khan ; Khurum Nazir Junejo
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：6
DOI：10.14569/IJACSA.2020.0110672
出版社：Science and Information Society (SAI)
摘要：Sentiment mining from the textual content on the web can give valuable insights for discernment, strategic decision making, targeted advertisement, and much more. Supervised machine learning (ML) approaches do not capture the sentiment inherent in the individual terms. Whereas the unsupervised sen-timent lexicon (SL) based approaches lag behind ML approaches because of a bias they have towards one sentiment than the other. In this paper, we propose a hybrid approach that uses unsuper-vised sentiment lexicons to transform the term space into a two-dimensional sentiment space on which a discriminative classifier is trained in a supervised fashion. This hybrid approach yields higher accuracy, faster training, and lower memory footprint than the ML approaches. It is more suitable for scenarios where training data is scarce. We support our claim by reporting results on six social media datasets using five sentiment lexicons and four ML algorithms.
关键词：Hybrid approach; machine learning; sentiment analysis; sentiment lexicons; sentiment space; social media analysis