Topic Sentiment Analysis in Online Learning Community from College Students

Purpose:Opinion mining and sentiment analysis in Online Learning Community can truly reflect the students’learning situation,which provides the necessary theoretical basis for following revision of teaching plans.To improve the accuracy of topic-sentiment analysis,a novel model for topic sentiment analysis is proposed that outperforms other state-of-art models.Methodology/approach:We aim at highlighting the identification and visualization of topic sentiment based on learning topic mining and sentiment clustering at various granularitylevels.The proposed method comprised data preprocessing,topic detection,sentiment analysis,and visualization.Findings:The proposed model can effectively perceive students’sentiment tendencies on different topics,which provides powerful practical reference for improving the quality of information services in teaching practice.Research limitations:The model obtains the topic-terminology hybrid matrix and the document-topic hybrid matrix by selecting the real user’s comment information on the basis of LDA topic detection approach,without considering the intensity of students’sentiments and their evolutionary trends.Practical implications:The implication and association rules to visualize the negative sentiment in comments or reviews enable teachers and administrators to access a certain plaint,which can be utilized as a reference for enhancing the accuracy of learning content recommendation,and evaluating the quality of their services.Originality/value:The topic-sentiment analysis model can clarify the hierarchical dependencies between different topics,which lay the foundation for improving the accuracy of teaching content recommendation and optimizing the knowledge coherence of related courses.

How Social Network Position Relates to Knowledge Building in Online Learning Communities?

Social Network Analysis,Statistical Analysis,Content Analysis and other research methods were used to research online learning communities at Capital Normal University,Beijing.Analysis of the two online courses resulted in the following conclusions:(1)Social networks of the two online courses form typical core-periphery structures;(2)Social networks of the two online courses contain“structural holes,”where some actors position themselves to become potential opinion-leaders within their social networks;(3)Actors,variously positioned within a core-periphery structure,show quite significant differences in terms of knowledge building;(4)Taking“structural holes”into account,there exist considerable differences in knowledge building between opinion-leaders and non opinion-leaders;(5)Actors in the“core”and“structural hole”positions have very different characteristics in terms of knowledge building.These actors in particular play important roles in online learning communities,impacting on the level of the constructed knowledge.

Improving Friend Recommendation for Online Learning with Fine-Grained Evolving Interest

Friend recommendation plays a key role in promoting user experience in online social networks(OSNs).However,existing studies usually neglect users’fine-grained interest as well as the evolving feature of interest,which may cause unsuitable recommendation.In particular,some OSNs,such as the online learning community,even have little work on friend recommendation.To this end,we strive to improve friend recommendation with fine-grained evolving interest in this paper.We take the online learning community as an application scenario,which is a special type of OSNs for people to learn courses online.Learning partners can help improve learners’learning effect and improve the attractiveness of platforms.We propose a learning partner recommendation framework based on the evolution of fine-grained learning interest(LPRF-E for short).We extract a sequence of learning interest tags that changes over time.Then,we explore the time feature to predict evolving learning interest.Next,we recommend learning partners by fine-grained interest similarity.We also refine the learning partner recommendation framework with users’social influence(denoted as LPRF-F for differentiation).Extensive experiments on two real datasets crawled from Chinese University MOOC and Douban Book validate that the proposed LPRF-E and LPRF-F models achieve a high accuracy(i.e.,approximate 50%improvements on the precision and the recall)and can recommend learning partners with high quality(e.g.,more experienced and helpful).