Deeper Sentiment Analysis Using Machine Translation Technology

Deeper Sentiment Analysis Using Machine Translation Technology KANAYAMA Hiroshi NASUKAWA Tetsuya WATANABE Hideo Tokyo Research Laboratory, IBM Japan, Ltd. 1623-14 Shimotsuruma, Yamato-shi, Kanagawa-ken, 242-8502 Japan {hkana,nasukawa,hiwat}@jp.ibm.com Abstract This paper proposes a new paradigm for senti- ment analysis: translation from text documents to a set of sentiment units. The techniques of deep language analysis for machine translation are applicable also to this kind of text mining task. We developed a high-precision sentiment analysis system at a low development cost, by making use of an existing transfer-based ma- chine translation engine. 1 Introduction Sentiment analysis (SA) (Nasukawa and Yi, 2003; Yi et al., 2003) is a task to obtain writers’ feelings as ex- pressed in positive or negative comments, questions, and requests, by analyzing large numbers of docu- ments. SA is becoming a useful tool for the com- mercial activities of both companies and individual consumers, because they want to sort out opinions about products, services, or brands that are scat- tered in online texts such as product review articles, replies given to questionnaires, and messages in bul- letin boards on the WWW. This paper describes a method to extract a set of sentiment units from sentences, which is the key component of SA. A sentiment unit is a tuple of a sentiment1, a predicate, and its arguments. For example, these sentences in a customer’s review of a digital camera (1) contained three sentiment units (1a), (1b), and (1c). Apparently these units indicate that the camera has good features in its lens and recharger, and a bad feature in its price. It has excellent lens, but the price is too high. I don’t think the quality of the recharger has any problem.    (1) [favorable] excellent (lens) (1a) [unfavorable] high (price) (1b) [favorable] problematic+neg (recharger) (1c) The extraction of these sentiment units is not a trivial task because many syntactic and semantic op- erations are required. First, the structure of a pred- icate and its arguments may be changed from the 1Possible values of a sentiment are ‘favorable’, ‘unfavor- able’, ‘question’, and ‘request’. In this paper the discussion is mostly focused on the first two values. syntactic form as in (1a) and (1c). Also modal, as- pectual, and negation information must be handled, as in (1c). Second, a sentiment unit should be con- structed as the smallest possible informative unit so that it is easy to handle for the organizing processes after extraction. In (1b) the degree adverb ‘too’ is omitted to normalize the expression. For (1c), the predicate ‘problematic’ has the argument ‘recharger’ instead of the head word of the noun phrase ‘the quality of the recharger’, because just using ‘qual- ity’ is not informative to describe the sentiment of the attribute of a real-world object. Moreover, dis- ambiguation of sentiments is necessary: in (1b) the adjective ‘high’ has the ‘unfavorable’ feature, but ‘high’ can be treated as ‘favorable’ in the expression “resolution is high”. We regard this task as translation from text to sentiment units, because we noticed …

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