Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
In this paper, we propose a method for classification and retrieval of news contents based on automatically extracted tags. There are some researches on automatic tagging that use TF-IDF to extract important tags in a news article. However, some useless tags are extracted, on the other hand the important ones are not extracted by the method. To solve this problem, the keywords extracted from the title and category of the article are added as a tag to the article e in addition to the tags extracted by TF-IDF. Besides, we performed an experiment that subjects extract keywords from each of the articles with different length. From the result of the experiment, we discuss the appropriate number of tags for the length of an article.
