Extracting Product Features from Online Consumer Reviews

Along with the exponential growth of user-generated content online comes the need of making sense of such content. Online consumer review is one type of user-generated content that has been more important. Thus, there is a demand for uncovering hidden patterns, unknown relationships and other useful information. The focal problem of this research is product feature extraction. Few existing studies has looked into detailed categorization of review features and explored how to adjust extraction methods by taking account of the characteristics of different categories of features. This paper begins with the introduction of a new scheme of feature classification and then introduces new extraction methods for each type of features separately. These methods were design to not only recognize new features but also filter irrelevant features. The experimental results show that our proposed methods outperform the state-of-the-art techniques

Mining Comparison Opinions from Chinese Online Reviews for Restaurant Competitive Analysis

Comparison is widely used by consumers during the process of product evaluation in order to emphasize their preference, which can contribute to a proxy for product competitiveness analysis. This paper proposes a novel method for mining comparative sentences based on the achievements of linguistic study. The definition of comparative sentence subcategory is put forward and a mixed rule pool containing both artificial rules and CSR is set up. Besides, an entity dictionary is used to re-check the identification result which can ensure precise identification and classification of comparative sentences. Real online comments are collected from Dianping.com as experimental data. The result shows that the proposed method outperforms baseline methods in terms of identification precision. Based on the result, features and opinions of comparative sentences are mined. We then conducted sentiment analysis to calculate the sentimental score of comparison relations. Finally, a feature competitive network of restaurants is constructed

Sentiment Analysis in Social Streams

In this chapter, we review and discuss the state of the art on sentiment analysis in social streams—such as web forums, microblogging systems, and social networks, aiming to clarify how user opinions, affective states, and intended emo tional effects are extracted from user generated content, how they are modeled, and howthey could be finally exploited.We explainwhy sentiment analysistasks aremore difficult for social streams than for other textual sources, and entail going beyond classic text-based opinion mining techniques. We show, for example, that social streams may use vocabularies and expressions that exist outside the mainstream of standard, formal languages, and may reflect complex dynamics in the opinions and sentiments expressed by individuals and communities

Sentiment Analysis in Social Streams

In this chapter we review and discuss the state of the art on sentiment analysis in social streams –such as web forums, micro-blogging systems, and so- cial networks–, aiming to clarify how user opinions, affective states, and intended emotional effects are extracted from user generated content, how they are modeled, and how they could be finally exploited. We explain why sentiment analysis tasks are more difficult for social streams than for other textual sources, and entail going beyond classic text-based opinion mining techniques. We show, for example, that social streams may use vocabularies and expressions that exist outside the main- stream of standard, formal languages, and may reflect complex dynamics in the opinions and sentiments expressed by individuals and communities

TOWARDS MINING BRAND ASSOCIATIONS FROM USER-GENERATED CONTENT (UGC): EVIDENCE FROM LINGUISTIC CHARACTERISTICS

Consumers’ brand associations offer qualitative explanations on a brand’s success or failure and are typically elicited using survey-based instruments. Marketers are interested in time- and cost-efficient, automated brand association elicitation approaches. To enable an automated brand association elicitation, we show that brand associations can be formalized and described by patterns of linguistic part-of-speech sequences that differ from ordinary speech which is required for an automated extraction via text mining. Furthermore, we provide evidence that UGC is an adequate data-source for an automated brand association elicitation. We do that by comparing survey-based and UGC data-sources using linguistic part-of-speech sequence- and n-gram analysis as well as sequential pattern mining. We contribute to exiting research by establishing prerequisites for the construction of novel information systems that use text mining to extract brand associations automatically from UGC

Mining Partially-Ordered Sequential Rules Common to Multiple Sequences

© 2015 IEEE. Sequential rule mining is an important data mining problem with multiple applications. An important limitation of algorithms for mining sequential rules common to multiple sequences is that rules are very specific and therefore many similar rules may represent the same situation. This can cause three major problems: (1) similar rules can be rated quite differently, (2) rules may not be found because they are individually considered uninteresting, and (3) rules that are too specific are less likely to be used for making predictions. To address these issues, we explore the idea of mining "partially-ordered sequential rules" (POSR), a more general form of sequential rules such that items in the antecedent and the consequent of each rule are unordered. To mine POSR, we propose the RuleGrowth algorithm, which is efficient and easily extendable. In particular, we present an extension (TRuleGrowth) that accepts a sliding-window constraint to find rules occurring within a maximum amount of time. A performance study with four real-life datasets show that RuleGrowth and TRuleGrowth have excellent performance and scalability compared to baseline algorithms and that the number of rules discovered can be several orders of magnitude smaller when the sliding-window constraint is applied. Furthermore, we also report results from a real application showing that POSR can provide a much higher prediction accuracy than regular sequential rules for sequence prediction