Road Map Approach to Automatic Topic Detection of Diaries - Volume 3 Number 2 (Apr. 2013) - IJMLC
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Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol.3(2): 237-239 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.310

Road Map Approach to Automatic Topic Detection of Diaries

F. H. Ismail
Abstract—The famous question on social networks "what is on your mind?" urges many of us to convert their thoughts and feelings into diaries. Detecting the topic of diaries is an interesting task to know people' interest. Many automatic methods have been introduced. The method used in this paper depends on preprocessing the diary words to generate a feature vector for each word. Then, the senses of each word are detected from the diary context by using CBC (clustering by committee) algorithm. CBC can avoid discovering duplicate senses and discover the less frequent senses of a word. The sense with the highest score is selected for each word. To detect the topic of the diary, the whole discovered senses are translated into concepts using the hierarchical concept taxonomy Wordnet. Concepts are traversed from bottom up to reach the most generalized concepts that express the topic of the diary. The previous approaches studied in this paper depend on the idea that the more frequent a word is used, the more important it is. The approach presented differs in that it incorporates the semantic dependencies among words because straightforward word counting misses the important concepts in the single document. This model can be used to explore people interests from their writings and can serve in e-marketing.

Index Terms—Topic description, topic discovery, concept discovery, document clustering.

The author is with Misr International University. Egypt (e-mail: fatma.helmy@miuegypt.edu.eg).

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Cite:F. H. Ismail, "Road Map Approach to Automatic Topic Detection of Diaries," International Journal of Machine Learning and Computing vol. 3, no. 2, pp. 237-239, 2013.

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