| opinion mining|
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|Related keyword(s)||data mining|
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opinion mining is included as keyword or extra keyword in 0 datasets, 0 tools and 3 publications.
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|Title||Author(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Hidden community detection based on microblog by opinion-consistent analysis||Fu M.-H.
|International Conference on Information Society, i-Society 2012||English||2012||The content or topic of post on the social network such as microblog, forum are usually reflected user's interests. Traditional community detection methods only consider explicit information of users. So that data analysis is limited in user predefined attributes. In order to solve this problem, a hidden community detection framework is proposed in this paper called opinion-consistent hidden community (OCHC) framework. Firstly, we collect and process post comments on facebook. Then, the post topic that the target user participated in can be defined through topic identification by the selected ontology, Wikipedia. Moreover, opinion-consistency between users and the target user is discovered by sentiment analysis. In brief, opinion mining and sentiment analysis are used to track the users who have the similar opinion on the specific topics. Besides, users focus on different features with different scopes on facebook can be found by multi-level OCHC framework that we proposed in this paper. Communities of opinion-consistent users are clustered Multi-level OCHC model. There are two major improvements of OCHC framework, one is that post topic is decided by topic identification instead of user-self, and the other is that user opinions are also considered during analysis phrase on OCHC framework. In experiment results, accuracy of topic identification promoted 5.5% than other methods and the time complexity reached 26 times faster than other one. On quantitative measurements of Polarity and Multi-Dimension sentiment analysis methods are performed well.||0||0|
|Towards a diversity-minded Wikipedia||Fabian Flöck
|WebSci Conference||English||June 2011||Wikipedia is a top-ten Web site providing a free encyclopedia created by an open community of volunteer contributors. As investigated in various studies over the past years, contributors have different backgrounds, mindsets and biases; however, the effects - positive and negative - of this diversity on the quality of the Wikipedia content, and on the sustainability of the overall project are yet only partially understood. In this paper we discuss these effects through an analysis of existing scholarly literature in the area and identify directions for future research and development; we also present an approach for diversity-minded content management within Wikipedia that combines techniques from semantic technologies, data and text mining and quantitative social dynamics analysis to create greater awareness of diversity-related issues within theWikipedia community, give readers access to indicators and metrics to understand biases and their impact on the quality of Wikipedia articles, and support editors in achieving balanced versions of these articles that leverage the wealth of knowledge and perspectives inherent to large-scale collaboration.||24||1|
|Sentiment analysis of news titles: The role of entities and a new affective lexicon||Loureiro D.
|Lecture Notes in Computer Science||English||2011||The growth of content on the web has been followed by increasing interest in opinion mining. This field of research relies on accurate recognition of emotion from textual data. There's been much research in sentiment analysis lately, but it always focuses on the same elements. Sentiment analysis traditionally depends on linguistic corpora, or common sense knowledge bases, to provide extra dimensions of information to the text being analyzed. Previous research hasn't yet explored a fully automatic method to evaluate how events associated to certain entities may impact each individual's sentiment perception. This project presents a method to assign valence ratings to entities, using information from their Wikipedia page, and considering user preferences gathered from the user's Facebook profile. Furthermore, a new affective lexicon is compiled entirely from existing corpora, without any intervention from the coders.||0||0|