Using Data Mining Techniques for Sentiment Shifter Identification

Using Data Mining Techniques for Sentiment Shifter Identification Samira Noferesti, Mehrnoush Shamsfard University of Sistan and Baluchestan, Shahid Beheshti University Iran E-mail: snoferesti@ece.usb.ac.ir, m-shams@sbu.ac.ir Abstract Sentiment shifters, i.e., words and expressions that can affect text polarity, play an important role in opinion mining. However, the limited ability of current automated opinion mining systems to handle shifters represents a major challenge. The majority of existing approaches rely on a manual list of shifters; few attempts have been made to automatically identify shifters in text. Most of them just focus on negating shifters. This paper presents a novel and efficient semi-automatic method for identifying sentiment shifters in drug reviews, aiming at improving the overall accuracy of opinion mining systems. To this end, we use weighted association rule mining (WARM), a well-known data mining technique, for finding frequent dependency patterns representing sentiment shifters from a domain-specific corpus. These patterns that include different kinds of shifter words such as shifter verbs and quantifiers are able to handle both local and long-distance shifters. We also combine these patterns with a lexicon-based approach for the polarity classification task. Experiments on drug reviews demonstrate that extracted shifters can improve the precision of the lexicon-based approach for polarity classification 9.25 percent. Keywords: sentiment analysis, sentiment shifters, association rule mining 1. Introduction Opinion mining, also called sentiment analysis, is the task of extracting and analyzing opinions, sentiments, evaluations or feelings from user-generated contents such as reviews, discussion groups and blogs. Due to its wide range of applications such as analysis of customer reviews (Hu & Liu, 2004) and reputation management (Wiegand et al., 2010), this field has received considerable attention both in industrial and academic research areas. One of the main subtasks of opinion mining is polarity classification, which aims at classifying opinions into predefined classes (usually positive and negative). Existing approaches to polarity classification can be grouped into two main categories: lexicon-based and machine learning approaches. Lexicon-based approaches mainly rely on linguistic resources containing polar terms and concepts such as SentiWordNet (Esuli & Sebastiani, 2006), General Inquirer (Stone et al., 1966) and Subjectivity Lexicon (Wilson et al., 2009). For example, “This drug is amazing.” is a positive sentence since the term “amazing” is positive in sentiment lexicons. However, these resources are not sufficient since the polarity classification is a challenging task that needs to tackle many subtle phenomena such as sentiment shifters. Sentiment shifters, also called valence shifters, are words and expressions that affect the polarity of an opinion by changing its magnitude or its direction. For example, in the sentence “I do not like this drug.”, the shifter word “not” before the positive word “like” changes the text polarity to negative. Therefore, ignoring sentiment shifters can lead to noticeable decline in overall accuracy of opinion mining systems. There are two types of shifter words or shifter trigger words: words that reverse the polarity of the given text (e.g., “no” and “never”), and words that change sentiment values by a constant amount (e.g., “severe” and “mild”). In this …

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