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With tags widely used in organizing and searching contents in massive data era, how to automatically generate appropriate tags of resource for users became a hot issue on social networks research. Tag recommendation for text resource can be modeled as a keyword extraction problem, hence topic modeling such as LDA
posts were collected from a selected hacker forum using a customized web-crawler. Posts were analyzed using a parts of speech tagger, which helped determine a list of keywords used to query the data. Next, a sentiment analysis tool scored these keywords, which were then analyzed to determine the effectiveness of this
and responses in MOOCs forum to extract keywords of misconceptions for instructors based on Natural Language Processing (NLP) technique. In this study, the researchers mining these misconceptions from over 120 thousands of single Chinese words and 15 thousands of English words in a course. Moreover, we visualize them by
This article describes an algorithm to facilitate the proper assignment of reviewers by finding an author's profile. It uses an original approach to analyzing publications published in digital libraries to get additional keywords based on NLP (natural language processing) techniques. Comparing profiles and finding
to collect active commercial entities and a commercial relation lexicon is created to collect keywords that flag commercial relations. Illustration and applications are also discussed, which undoubtedly discloses a promising future of commercial network study.
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