By Dell Zhang, Karl Prior, Mark Levene, Robert Mao, Diederik van Liere (auth.), Shuigeng Zhou, Songmao Zhang, George Karypis (eds.)
This publication constitutes the refereed complaints of the eighth overseas convention on complicated info Mining and purposes, ADMA 2012, held in Nanjing, China, in December 2012. The 32 commonplace papers and 32 brief papers provided during this quantity have been conscientiously reviewed and chosen from 168 submissions. they're prepared in topical sections named: social media mining; clustering; desktop studying: algorithms and purposes; category; prediction, regression and popularity; optimization and approximation; mining time sequence and streaming info; internet mining and semantic research; info mining purposes; seek and retrieval; info suggestion and hiding; outlier detection; subject modeling; and information dice computing.
Read or Download Advanced Data Mining and Applications: 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012. Proceedings PDF
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Additional info for Advanced Data Mining and Applications: 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012. Proceedings
Our contribution, in this paper, is to combine both the highlevel-topics model and the user communities extraction in a uniﬁed approach called CETD: Community Extraction based on Topic-Driven-model. For instance, if we consider a user who writes always tweets in the domain sports like the following real-word tweet: ”Contracts for Top College Football Coaches Grow Complicated ”. Our proposed topic-driven model will assign automatically the topics ”football” to this tweet. Now, if we consider another user who writes the following real-world tweet: ”Barcelona win 2-0 at Real Mallorca but Real Madrid return to form and smash Real Sociedad 5-1 ”.
A great number of new words are emerging in the online social media every day. 0 texts. Some widely used typos and phonetic substitutions evolve into new sentiment-bearing words. Emerging Meanings. Even the same word may have diﬀerent explanations or sentiment orientations at diﬀerent time periods. Coverage. Due to the above two challenges, the traditional thesaurus and knowledge base, such as WordNet, usually suﬀer from the coverage problem. 0 based social media. Everyday, enormous numbers of text posts that contain people’s rich sentiments are published in microblogging websites such as Twitter and Weibo .
Therefore, we calculate the DD value of each word in the learned lexicon. Given a threshold , if DD(w) < α, we eliminate w from the learned lexicon because it does not show an obvious orientation to either of the sentiment categories. To evaluate the performance of the lexicon learning and the optimization methods, we propose a lexicon based sentiment analysis algorithm for Chinese microblog, which is given in Algorithm 1. In Algorithm 1, the number of matched positive and negative words from the lexicon is counted and the negation words are also considered during the classiﬁcation.