Fuzzy Based Implicit Sentiment Analysis on Quantitative Sentences

Amir Hossein Yazdavar, Monireh Ebrahimi, Naomie Salim


With the rapid growth of social media on the web, emotional polarity computation has become a flourishing frontier in the text mining community. However, it is challenging to understand the latest trends and summarize the state or general opinions about products due to the big diversity and size of social media data and this creates the need of automated and real time opinion extraction and mining. On the other hand, the bulk of current research has been devoted to study the subjective sentences which contain opinion keywords and limited work has been reported for objective statements that imply sentiment. In this paper, fuzzy based knowledge engineering model has been developed for sentiment classification of special group of such sentences including the change or deviation from desired range or value. Drug reviews are the rich source of such statements. Therefore, in this research, some experiments were carried out on patient’s reviews on several different cholesterol lowering drugs to determine their sentiment polarity. The main conclusion through this study is, in order to increase the accuracy level of existing drug opinion mining systems, objective sentences which imply opinion should be taken into account.


Test mining, Natural Language Processing, Sentiment Analysis, Fuzzy Set Theory

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