SESM: Emotional Social Semantic and Time Series Analysis of Learners’ Comments

In contrast to traditional web search, personalized search provides search results that take into account the user’s preferences. However, the existing personalized search methods have limitations in providing appropriate search results for the individual’s preferences, because they do not consider the user’s recent preferences or the preferences of other users. In this paper, we propose a new search method considering the user’s recent preferences and similar users’ preferences on social media analysis. Since the user expresses personal opinions on social media, it is possible to grasp the user preferences when analyzing the records of social media activities. The proposed method collects user social activity records and determines keywords of interest using TF-IDF. Since user preferences change continuously over time, we assign time weights to keywords of interest, giving many high values to state-of-the-art user preferences. We identify users with similar preferences to extend the search results to be provided to users because considering only user preferences in personalized searches can provide narrow search results. The proposed method provides personalized search results considering social characteristics by applying a ranking algorithm that considers similar user preferences as well as user preferences. It is shown through various performance evaluations that the proposed personalized search method outperforms the existing methods.

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SESM: Emotional Social Semantic and Time Series Analysis of Learners’ Comments

Cited by 2 publications

References 15 publications

Analyzing User Feedback in Massive Open Online Courses: A Bibliometrics-Based Systematic Review

Analyzing User Feedback in Massive Open Online Courses: A Bibliometrics-Based Systematic Review

Personalized Search Using User Preferences on Social Media

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