2004 MiningAndSummCustomerReviews
- (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). doi:10.1145/1014052.1014073
Subject Headings: Opinion Mining Task, Opinion Mining Algorithm, Customer Review, Opinion Word.
Notes
- It proposes a lexicon-based method that uses opinion bearing words. (Ding et al., 2008)
Cited By
- ~755 http://scholar.google.com/scholar?q=%22Mining+and+Summarizing+Customer+Reviews%22+2004
- ~269 http://dl.acm.org/citation.cfm?doid=1014052.1014073#citedby
2008
- (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 Search and Web Data Mining (WSDM 2008). doi:10.1145/1341531.1341561
- QUOTE: In [13, 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/) [8]. 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 [13]. 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.
- (Pang & Lee, 2008) ⇒ Bo Pang, and Lillian Lee. (2008). “Opinion Mining and Sentiment Analysis.” Now Publishers Inc. ISBN:1601981503
Quotes
Author Keywords
reviews, sentiment classification, summarization, text mining.
Abstract
Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.
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