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There has been a rapid rise in the number of users getting connected online via social networking sites. To communicate with other users and share their thoughts and opinions, online users' tend to use texts in the form of blogs, posts, tweets, messages, reviews, comments etc. Thus, there has been an immense possibility complemented with a wide gamut of research in the field of Opinion Mining or Sentiment...
There are many opportunities and challenges in data analytic research for TCM (Traditional Chinese Medicine) in advent of big data era, like various clinical record sources, different symptom descriptions, lots of collected clinical symptoms, more than one syndrome attached to one clinical record and etc. Novel methods on support vector machines, ensemble learning, feature selection, multi-label learning...
Text classification is one of the most significant contents in Natural Language Processing research field. In most real cases, text classification is usually a multi-label learning task. Currently, there are three mainstream attribute measures (i.e., information gain, document frequency and chi-square test values) which are often used to describe documents. The three attribute measures have been applied...
A new text classification algorithm has been put forward based on basic support vector machine algorithm. The SVM-KNN algorithm for text classification has been proposed which combined SVM algorithm and KNN algorithm. The SVM-KNN algorithm can improve the performance of classifier by the feedback and improvement of classifying prediction probability. The actual effect of SVM-KNN algorithm is tested...
Feature selection and feature weight calculating are key preprocesses in text classification. A new feature selection approach based on average interaction gain (AIG) is presented and a new feature weight adjustment technique (WA) taking inter-class distribution and intra-class distribution into consideration is presented too. Then a new approach combining AIG with WA called AIG-WA is presented. In...
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