By Dell Zhang, Karl Prior, Mark Levene, Robert Mao, Diederik van Liere (auth.), Shuigeng Zhou, Songmao Zhang, George Karypis (eds.)
This e-book constitutes the refereed lawsuits of the eighth foreign convention on complicated information Mining and functions, ADMA 2012, held in Nanjing, China, in December 2012. The 32 ordinary papers and 32 brief papers provided during this quantity have been rigorously reviewed and chosen from 168 submissions. they're geared up in topical sections named: social media mining; clustering; laptop studying: algorithms and functions; class; prediction, regression and popularity; optimization and approximation; mining time sequence and streaming info; internet mining and semantic research; info mining purposes; seek and retrieval; details suggestion and hiding; outlier detection; subject modeling; and knowledge dice computing.
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Additional info for Advanced Data Mining and Applications: 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012. Proceedings
Feng et al. 0 based social media. Emerging Words. 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.
The results show that the proposed method is able to extract the emerging words, and learned lexicon outperforms two well-known Chinese lexicons in classifying the sentiments in microblogs. 0 based social media, how to provide an eﬃcient way to analyze users’ sentiment has become a major concern for both academic researchers and commercial companies. In sentiment analysis, one fundamental problem is to recognize whether a given word expresses positive or negative meaning. Although machine learning based algorithms have become commonplace in the related literatures , the sentiment lexicon still plays an important role for many sentiment analysis tasks.
5 here. We can see from Table 1 that almost all the lexicons have balanced number of positive and negative words except NTUSD, which contains much more negative words . About two thousand words are eliminated in the Optimized Lexicon. Fig. 2. The F -Score with diﬀerent size of the learned lexicon Table 1. Lexicon size HowNet NTUSD Learned Lexicon Optimized Lexicon Positive Words 4,528 Negative Words 4,320 2,813 8,277 5,022 4,986 4,066 4,122 36 S. Feng et al. Classiﬁcation Performance. We compare our learned lexicon with two famous Chinese sentiment lexicons HowNet  and NTUSD.
Advanced Data Mining and Applications: 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012. Proceedings by Dell Zhang, Karl Prior, Mark Levene, Robert Mao, Diederik van Liere (auth.), Shuigeng Zhou, Songmao Zhang, George Karypis (eds.)