text: Product reviews contain a lot of useful information about product features and customer opinions. One important product feature is the complementary entity (products) that may potentially work together with the reviewed product. Knowing complementary entities of the reviewed product is very important because customers want to buy compatible products and avoid incompatible ones. In this paper, we address the problem of Complementary Entity Recognition (CER). Since no existing method can solve this problem, we first propose a novel unsupervised method to utilize syntactic dependency paths to recognize complementary entities. Then we expand category-level domain knowledge about complementary entities using only a few general seed verbs on a large amount of unlabeled reviews. The domain knowledge helps the unsupervised method to adapt to different products and greatly improves the precision of the CER task. The advantage of the proposed method is that it does not require any labeled data for training. We conducted experiments on 7 popular products with about 1200 reviews in total to demonstrate that the proposed approach is effective.
  @cite_14: 1. Introduction 2. The problem of sentiment analysis 3. Document sentiment classification 4. Sentence subjectivity and sentiment classification 5. Aspect sentiment classification 6. Aspect and entity extraction 7. Sentiment lexicon generation 8. Analysis of comparative opinions 9. Opinion summarization and search 10. Analysis of debates and comments 11. Mining intentions 12. Detecting fake or deceptive opinions 13. Quality of reviews.
  @cite_6: 
  @cite_0: 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.
  @cite_19: Aspect extraction aims to extract fine-grained opinion targets from opinion texts. Recent work has shown that the syntactical approach, which employs rules about grammar dependency relations between opinion words and aspects, performs quite well. This approach is highly desirable in practice because it is unsupervised and domain independent. However, the rules need to be carefully selected and tuned manually so as not to produce too many errors. Although it is easy to evaluate the accuracy of each rule automatically, it is not easy to select a set of rules that produces the best overall result due to the overlapping coverage of the rules. In this paper, we propose a novel method to select an effective set of rules. To our knowledge, this is the first work that selects rules automatically. Our experiment results show that the proposed method can select a subset of a given rule set to achieve significantly better results than the full rule set and the existing state-of-the-art CRF-based supervised method.
  @cite_15: 
  @cite_10: Analysis of opinions, known as opinion mining or sentiment analysis, has attracted a great deal of attention recently due to many practical applications and challenging research problems. In this article, we study two important problems, namely, opinion lexicon expansion and opinion target extraction. Opinion targets (targets, for short) are entities and their attributes on which opinions have been expressed. To perform the tasks, we found that there are several syntactic relations that link opinion words and targets. These relations can be identified using a dependency parser and then utilized to expand the initial opinion lexicon and to extract targets. This proposed method is based on bootstrapping. We call it double propagation as it propagates information between opinion words and targets. A key advantage of the proposed method is that it only needs an initial opinion lexicon to start the bootstrapping process. Thus, the method is semi-supervised due to the use of opinion word seeds. In evaluation, we compare the proposed method with several state-of-the-art methods using a standard product review test collection. The results show that our approach outperforms these existing methods significantly.
  @cite_12: 
  @cite_20: This paper studies a text mining problem, comparative sentence mining (CSM). A comparative sentence expresses an ordering relation between two sets of entities with respect to some common features. For example, the comparative sentence "Canon's optics are better than those of Sony and Nikon" expresses the comparative relation: (better, optics , Canon , Sony, Nikon ). Given a set of evaluative texts on the Web, e.g., reviews, forum postings, and news articles, the task of comparative sentence mining is (1) to identify comparative sentences from the texts and (2) to extract comparative relations from the identified comparative sentences. This problem has many applications. For example, a product manufacturer wants to know customer opinions of its products in comparison with those of its competitors. In this paper, we propose two novel techniques based on two new types of sequential rules to perform the tasks. Experimental evaluation has been conducted using different types of evaluative texts from the Web. Results show that our techniques are very promising.
summary: The proposed problem is closely related to aspect extraction @cite_0 @cite_12 @cite_10 @cite_14 , which is to extract product features from reviews. More specifically, extracting comparable products (i.e, one type of substitutes, or products that can replace each other) from reviews is studied by Jindal and Liu @cite_20 . Recently, dependency paths @cite_15 are used for aspect extraction @cite_10 @cite_19 . @cite_6 use unsupervised graph labeling method to identify entities from opinion targets. However, since aspects are mostly context independent and the same aspect may appear multiple times, aspect extraction in general does not need to extract each occurrence of an aspect (as long as the same aspect can be extracted at least once). In contrast, the CER problem is context dependent and many complementary entities are infrequent (i.e., is infrequent than the aspect ). We use dependency paths to accurately identify each occurrence of complementary entities. Since extracting each complementary entity can be inaccurate, we further utilize domain knowledge to improve the precision.