Research In Sentiment Strength Fuzziness With Opinion Mining General Framework

From the perspective of epistemology, information can be divided into objective information and subjective information. The former describes the facts and the latter reflects viewpoints and attitudes of the individuals or organizations.In the past, ordinary Internet users usually browse and receive information, as a result, what people demanded for information is mostly objective information. With the development of the Internet, more people contribute to the creation and distribution of information on the Internet, people's needs for the subjective information becomes more popular now. The flourish of the Internet has generated massive volumes of subjective information. But the cost of finding and using the information is quite high, which causes urgent need to analyze subjective information.Opinion mining recognizes and analyzes subjective information by using techniques such as natural language processing, information extraction, data mining and so on. It emergence meets people's needs for subjective information analysis and it has been applied to word-of-mouth analysis, public opinions analysis, enterprise competitive intelligence and etc.Opinion mining has achieved abundant research results, but it still faced with the following problems:1) complexity of the information,2) various forms of information,3) lack of standard languages,4) fuzziness,5) domain dependence, etc. The fourth problem has important academic significance and practical values. If solved reasonably it will contribute to a comprehensive real response of the subjective information.This paper studies sentiment strength fuzziness by using fuzzy mathematics and evidence theory. It considers sentiment strength as fuzzy sets, using membership degree to express sentiment strength and using fuzzy statistical method to calculate membership degree. The opinion summarization is regarded as decision-making issues in which Dempster-Shafer's rule of combination is used to combine opinions.To attain these goals, the author built up an experimental platform based on a general framework for opinion mining, and took washing machines for example to provide reference basic data for research and comparison. Based on the experimental platform, the procedures and effects of the method are illustrated with examples.The dissertation is composed of six chapters as follows.(1) OverviewsThis chapter summarizes the status of the Opinion mining and sentiment strength research. The techniques of Opinion mining are divided into two categories. One type is focused on the classification, which is based on sentiment orientation of sentence; the other type tends to extract and analyze features of the viewpoints. During the research, we found it difficult to reach an agreement on sentiment orientation and sentiment strength between corpus annotators and linguists, which shows fuzziness. At present, the researches on sentiment orientation fuzziness have made some achievements, but the study on sentiment strength fuzziness is rare.(2) Opinion Mining General FrameworkThis chapter establishes an experimental platform by Opinion mining general framework. We illustrates core parts of framework using 20 washing machines as test objects, and a total of 6006 subjective records comes from JingdongShangCheng Website. Using feature and sentiment word selection tools, extract 22 features and 229 sentiment words. Then generate analysis results in web pages. The accuracy rate is 88.47%.(3) Analysis of Sentiment Strength FuzzinessThis chapter aims to analyze sentiment strength fuzziness. Both objects that people express their viewpoints towards and the languages as the means, for people to express their viewpoints exhibit fuzziness. So the analysis also tends to be fuzzy. In the opinion mining research, sentiment orientation shows ambiguity, rather than fuzziness, which can be resolved by eliminating domain-dependence, but sentiment strength cannot.(4) Representation of Sentiment Strength FuzzinessThis chapter aims to introduce a representation method of sentiment strength fuzziness. Under the guidance of fuzzy set theory, this paper considers the grades of sentiment strength as fuzzy sets, using membership degrees to express how sentiment strength belonging to a certain strength grade, and makes use of fuzzy statistical method to calculate membership degrees. Take adverbs and sentiment words in Chapter 2 as example to illustrate the method.(5) Combination of Sentiment Strength FuzzinessThis chapter aims to introduce combination of sentiment strength fuzziness. Under the guidance of evidence theory, the opinion summarization is regarded as decision-making issues, and Dempster-Shafer's rule of combination is used to combine opinions. Based on the results in Chapter 4, an example illustrates the combination method by taking the "Panasonic XQB60-P620U6" washing machine as test object. (6) ConclusionThis chapter summarizes the research contents and prospects. Based on comparative analysis of the new results in Chapter 5 and old results in Chapter 2, this paper prove that the new method reasonable and valid. Finally, this paper discusses the further research.