Retrieving Articles and Image Labeling Based on Relevance of Keywords

When users input keywords into the search engine, a massive search results will be retrieved. However, it becomes difficult for the users to learn as it is unreadable with the excessive amount of results. This study establishes an information retrieval system for computer science related articles. It firstly collects articles by running a web crawler, and uses TF-IDF (Term Frequency-Inverse Document Frequency) method to extract keywords to acquire the focus of the article. And with the use of association rules and cosine similarity, the articles are classified by their relevance. Finally, according to users' feedbacks, the system provides appropriate resources to improve the motivation and willingness to learn. In addition, the pictures in the articles are also a basis for analyzing the articles. This study uses image semantic analysis to label the pictures so as to improve the accuracy in analyzing the articles.