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- AKA: Opinion Bearing Word.
- "weight", as in "its weight is 5.3kg".
- See: Factual Sentence.
- (Ding et al., 2008) ⇒ Xiaowen Ding, Bing Liu, and Philip S. Yu. (2008). “A Holistic Lexicon-based Approach to Opinion Mining.” In: Proceedings of the International Conference on Web Wearch and Web Data Mining (WSDM 2008).
- (Hu & Liu, 2004) ⇒ Minqing Hu, and Bing Liu . (2004). “Mining and Summarizing Customer Reviews.” In: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004).
- In , a lexicon-based method is proposed to use opinion bearing words (or simply opinion words) to perform task (2). Opinion words are words that are commonly used to express positive or negative opinions (or sentiments), e.g., “amazing”, “great”, “poor” and “expensive”. The method basically counts the number of positive and negative opinion words that are near the product feature in each review sentence. If there are more positive opinion words than negative opinion words, the final opinion on the feature is positive and otherwise negative. The opinion lexicon or the set of opinion words was obtained through a bootstrapping process using WordNet (http://wordnet.princeton.edu/) . This method is simple and efficient, and gives reasonable results. However, this technique has some major shortcomings.
- First of all, it does not have an effective mechanism for dealing with context dependent opinion words. There are many such words. For example, the word “small” can indicate a positive or a negative opinion on a product feature depending on the product feature and the context. There is probably no way to know the semantic orientation of a context dependent opinion word by looking at only the word and the product feature that it modifies without prior knowledge of the product or the product feature. Asking a domain expert or user to provide such knowledge is not scalable due to the huge number of products, product features and opinion words. Several researchers have attempted the problem [11, 16, 28]. However, their approaches still have some major limitations as we will see in the next section. In this paper, we propose a holistic lexicon-based approach to solving the problem, which improves the lexicon-based method in . Instead of looking at the current sentence alone, this approach exploits external information and evidences in other sentences and other reviews, and some linguistic conventions in natural language expressions to infer orientations of opinion words. No prior domain knowledge or user inputs are needed. Based on our experiment results, we are fairly confident to say that context dependent opinion words no longer present a major problem.
- Second, when there are multiple conflicting opinion words in a sentence, existing methods are unable to deal with them well. We propose a new method to aggregate orientations of such words by considering the distance between each opinion word and the product feature. This turns out to be highly effective.