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Topic Sentiment Analysis in Online Learning Community from College Students

 and    | May 20, 2020

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Kaklauskas, A., Zavadskas, E.K., Seniut, M., et al. (2013). Recommender system to analyze student’s academic performance. Expert Systems with Applications an International Journal, 40(15), 6150–6165.KaklauskasA.ZavadskasE.K.SeniutM.2013Recommender system to analyze student’s academic performanceExpert Systems with Applications an International Journal40156150616510.1007/978-3-319-13659-2_7Search in Google Scholar

Blei, D.M., Ng, A.Y., & Jordan, M.I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(9), 993–1022.BleiD.M.NgA.Y.JordanM.I.2003Latent dirichlet allocationJournal of Machine Learning Research399931022Search in Google Scholar

Cho, M.H., Kim, Y., & Choi, D.H. (2017). The effect of self-regulated learning on college students’ perceptions of community of inquiry and affective outcomes in online learning. Internet & Higher Education, 34, 10–17.ChoM.H.KimY.ChoiD.H.2017The effect of self-regulated learning on college students’ perceptions of community of inquiry and affective outcomes in online learningInternet & Higher Education34101710.1016/j.iheduc.2017.04.001Search in Google Scholar

Chu, K.M., & Li, F. (2010). Topic evolution based on LDA and topic association. J. Shanghai Jiao tong Univ. (Sci), 44(11), 1501–1506.ChuK.M.LiF.2010Topic evolution based on LDA and topic associationJ. Shanghai Jiao tong Univ. (Sci)441115011506Search in Google Scholar

Cerulo, L., & Distante, D. (2013). Topic-driven semi-automatic reorganization of online discussion forums: A case study in an e-learning context. Global Engineering Education Conference. IEEE, March 13–15, Berlin, Germany. pp 289–298.CeruloL.DistanteD.2013Topic-driven semi-automatic reorganization of online discussion forums: A case study in an e-learning contextGlobal Engineering Education Conference. IEEEMarch 13–15Berlin, Germany28929810.1109/EduCon.2013.6530121Search in Google Scholar

Chen, Y.C. (2018). A novel algorithm for mining opinion leaders in social networks. World Wide Web-internet & Web Information Systems, 2, 1–17.ChenY.C.2018A novel algorithm for mining opinion leaders in social networksWorld Wide Web-internet & Web Information Systems211710.1007/s11280-018-0586-xSearch in Google Scholar

Ethem, F.C., Aysu, E.C., & Fazli, C. (2018). Multilingual sentiment analysis: An RNN-based framework for limited data. In Proceedings of ACM SIGIR 2018 Workshop on Learning from Limited or Noisy Data, July 12, Michigan, USA, pp 1–5.EthemF.C.AysuE.C.FazliC.2018Multilingual sentiment analysis: An RNN-based framework for limited dataInProceedings of ACM SIGIR 2018 Workshop on Learning from Limited or Noisy DataJuly 12Michigan, USA15Search in Google Scholar

Fariza, K. (2019). Students’ identities and its relationships with their engagement in an Online Learning Community. International Journal of Emerging Technologies in Learning, 14(5), 4–19.FarizaK.2019Students’ identities and its relationships with their engagement in an Online Learning CommunityInternational Journal of Emerging Technologies in Learning14541910.3991/ijet.v14i05.8196Search in Google Scholar

Colace, F., De Santo, M., & Greco, L. (2014). SAFE: A sentiment analysis framework for e-Learning. International Journal Of Emerging Technologies In Learning (IJET), 9(6), pp. 37–41. doi: http://dx.doi.org/10.3991/ijet.v9i6.4110ColaceF.De SantoM.GrecoL.2014SAFE: A sentiment analysis framework for e-LearningInternational Journal Of Emerging Technologies In Learning (IJET)963741http://dx.doi.org/10.3991/ijet.v9i6.411010.3991/ijet.v9i6.4110Search in Google Scholar

Ficamos, P., Yan, L., & Chen, W. (2013). A naive bayes and maximum entropy approach to sentiment analysis: Capturing domain-specific data in Weibo. IEEE International Conference on Big Data & Smart Computing. February 13–16, Jeju, South Korea. pp 324–332.FicamosP.YanL.ChenW.2013A naive bayes and maximum entropy approach to sentiment analysis: Capturing domain-specific data in WeiboIEEE International Conference on Big Data & Smart ComputingFebruary 13–16Jeju, South Korea324332Search in Google Scholar

Gao, G., Luo J.M., & Wang, Y. (2017). Analyzing textual sentiment based on HNC theory. Data Analysis and Knowledge Discovery, 8(8), 85–90.GaoG.LuoJ.M.WangY.2017Analyzing textual sentiment based on HNC theoryData Analysis and Knowledge Discovery888590Search in Google Scholar

Ge, G., Chen, L., & Du, J. (2013). The research on topic detection of microblog based on TC-LDA. IEEE International Conference on Communication Technology. November 17–19, Guilin, China. pp 257–262.GeG.ChenL.DuJ.2013The research on topic detection of microblog based on TC-LDAIEEE International Conference on Communication TechnologyNovember 17–19Guilin, China257262Search in Google Scholar

Ghiasifard, S., Khadivi, S., Asadpour, M., et al. (2015). Improving the quality of overlapping community detection through link addition based on topic similarity. International symposium on Artificial Intelligence & Signal Processing (AISP 2015), March 3–5, Mashhad, Iran. pp 244–250.GhiasifardS.KhadiviS.AsadpourM.2015Improving the quality of overlapping community detection through link addition based on topic similarityInternational symposium on Artificial Intelligence & Signal Processing (AISP 2015)March 3–5Mashhad, Iran24425010.1109/AISP.2015.7123518Search in Google Scholar

Hofmann, T. (1999). Probabilistic latent semantic indexing. International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, pp 50–57.HofmannT.1999Probabilistic latent semantic indexingInternational ACM SIGIR Conference on Research and Development in Information Retrieval. ACM505710.1145/312624.312649Search in Google Scholar

Kohoulat, N., Hayat, A.A., Dehghani, M.R., Kojuri, J., & Amini, M. (2017). Medical students’ academic emotions: the role of perceived learning environment. Journal of Advances in Medical Education & Professionalism, 5(2), 78–83.KohoulatN.HayatA.A.DehghaniM.R.KojuriJ.AminiM.2017Medical students’ academic emotions: the role of perceived learning environmentJournal of Advances in Medical Education & Professionalism527883Search in Google Scholar

Li, X.D., Ba, Z.C., & Huang, L. (2015). A text feature selection method based on weighted latent dirichlet allocation and multi-granularity. New Technology of Library and Information Service, 258, 42–49.LiX.D.BaZ.C.HuangL.2015A text feature selection method based on weighted latent dirichlet allocation and multi-granularityNew Technology of Library and Information Service2584249Search in Google Scholar

Li, Y., Ma, S., Zhang, Y, Huang, R, & Kinshuk. (2013). An improved mix framework for opinion leader identification in online learning communities. Knowledge-Based Systems, 43(2), 43–51.LiY.MaS.ZhangYHuangRKinshuk2013An improved mix framework for opinion leader identification in online learning communitiesKnowledge-Based Systems432435110.1016/j.knosys.2013.01.005Search in Google Scholar

Liu, Y., Bi, J.W., & Fan, Z.P. (2017). A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm. Information Sciences, 394, 38–52.LiuY.BiJ.W.FanZ.P.2017A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithmInformation Sciences394385210.1016/j.ins.2017.02.016Search in Google Scholar

Lu, Y., Zhang, P., Liu, J., Li, J., & Deng, S. (2013). Health-related hot topic detection in online communities using text clustering. PLoS ONE, 8(2), e56221.LuY.ZhangP.LiuJ.LiJ.DengS.2013Health-related hot topic detection in online communities using text clusteringPLoS ONE82e5622110.1371/journal.pone.0056221357413923457530Search in Google Scholar

Martíneztorres, M.R. (2015). Content analysis of open innovation communities using latent semantic indexing. Technology Analysis & Strategic Management, 27(7), 859–875.MartíneztorresM.R.2015Content analysis of open innovation communities using latent semantic indexingTechnology Analysis & Strategic Management27785987510.1080/09537325.2015.1020056Search in Google Scholar

Meng, Z.Q., Shen, S.M., & Chen, Q.L. (2013). A network decomposition-based text clustering algorithm for topic detection. Applied Mechanics & Materials, 239, 1318–1323.MengZ.Q.ShenS.M.ChenQ.L.2013A network decomposition-based text clustering algorithm for topic detectionApplied Mechanics & Materials2391318132310.4028/www.scientific.net/AMM.239-240.1318Search in Google Scholar

Mertiya, M., & Singh, A. (2016). Combining naive bayes and adjective analysis for sentiment detection on Twitter. 2016 IEEE International Conference on Inventive Computation Technologies (ICICT). August 26–27, Coimbatore, India. pp 157–163.MertiyaM.SinghA.2016Combining naive bayes and adjective analysis for sentiment detection on Twitter2016 IEEE International Conference on Inventive Computation Technologies (ICICT)August 26–27Coimbatore, India15716310.1109/INVENTIVE.2016.7824847Search in Google Scholar

Cheng, M.M., Su, C.Y., Zhang, J.P., & Yang, Y. (2015). Analyzing temporal patterns of groups and individuals in an online learning forum. International Journal of Emerging Technologies in Learning, 10(5), 66–71.ChengM.M.SuC.Y.ZhangJ.P.YangY.2015Analyzing temporal patterns of groups and individuals in an online learning forumInternational Journal of Emerging Technologies in Learning105667110.3991/ijet.v10i5.4722Search in Google Scholar

Hady, M.F.A., & Schwenker, F. (2008). Co-training by Committee: A New Semi-supervised Learning Framework. Workshops IEEE International Conference on Data Mining. IEEE.HadyM.F.A.SchwenkerF.2008Co-training by Committee: A New Semi-supervised Learning FrameworkWorkshops IEEE International Conference on Data Mining. IEEE10.1109/ICDMW.2008.27Search in Google Scholar

Nagori, R., & Aghila, G. (2012). LDA based integrated document recommendation model for e-learning systems, IEEE International Conference on Emerging Trends in Networks & Computer Communications. April 22–24, Udaipur, INDIA. pp 204–215.NagoriR.AghilaG.2012LDA based integrated document recommendation model for e-learning systemsIEEE International Conference on Emerging Trends in Networks & Computer CommunicationsApril 22–24Udaipur, INDIA20421510.1109/ETNCC.2011.6255892Search in Google Scholar

Nan, L., & Wu, D.D. (2010). Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision Support Systems, 48(2), 354–368.NanL.WuD.D.2010Using text mining and sentiment analysis for online forums hotspot detection and forecastDecision Support Systems48235436810.1016/j.dss.2009.09.003Search in Google Scholar

Qodmanan, H.R., Nasiri, M., & Minaei-Bidgoli, B. (2011). Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence. Expert Systems with Applications, 38(1), 288–298.QodmananH.R.NasiriM.Minaei-BidgoliB.2011Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidenceExpert Systems with Applications38128829810.1016/j.eswa.2010.06.060Search in Google Scholar

Parvathy, G., & Bindhu, J.S. (2016). A probabilistic generative model for mining cybercriminal network from online social media: A review. International Journal of Computer Applications, 134(14), 1–4.ParvathyG.BindhuJ.S.2016A probabilistic generative model for mining cybercriminal network from online social media: A reviewInternational Journal of Computer Applications134141410.5120/ijca2016908121Search in Google Scholar

Pappas, N., Redi, M., Topkara, M., et al. (2017). Multilingual visual sentiment concept clustering and analysis. International Journal of Multimedia Information Retrieval, 6(1), 51–70.PappasN.RediM.TopkaraM.2017Multilingual visual sentiment concept clustering and analysisInternational Journal of Multimedia Information Retrieval61517010.1007/s13735-017-0120-4Search in Google Scholar

Ren, R., Ling, W., & Yao, Y. (2018). An analysis of three types of partially-known formal concepts. International Journal of Machine Learning & Cybernetics, 9(11), 1767–1783.RenR.LingW.YaoY.2018An analysis of three types of partially-known formal conceptsInternational Journal of Machine Learning & Cybernetics9111767178310.1007/s13042-017-0743-zSearch in Google Scholar

Santosh, D.T., Vardhan, B.V., & Ramesh, D. (2016). Extracting product features from reviews using feature ontology tree applied on LDA topic clusters. IEEE 6th International Conference on Advanced Computing (IACC), February 27–28, Bhimavaram, India. pp 89–96.SantoshD.T.VardhanB.V.RameshD.2016Extracting product features from reviews using feature ontology tree applied on LDA topic clustersIEEE 6th International Conference on Advanced Computing (IACC)February 27–28Bhimavaram, India899610.1109/IACC.2016.39Search in Google Scholar

Shirakawa, M., Nakayama, K., Hara, T., et al. (2017). Wikipedia-based semantic similarity measurements for noisy short texts using extended naive bayes. IEEE Transactions on Emerging Topics in Computing, 3(2), 205–219.ShirakawaM.NakayamaK.HaraT.2017Wikipedia-based semantic similarity measurements for noisy short texts using extended naive bayesIEEE Transactions on Emerging Topics in Computing3220521910.1109/TETC.2015.2418716Search in Google Scholar

Shin, B., Lee, T., & Choi, J.D. (2017). Lexicon integrated CNN models with attention for sentiment analysis. Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, September 7–11, Copenhagen, Denmark, pp 149–158.ShinB.LeeT.ChoiJ.D.2017Lexicon integrated CNN models with attention for sentiment analysisProceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media AnalysisSeptember 7–11Copenhagen, Denmark14915810.18653/v1/W17-5220Search in Google Scholar

Shea, P., Li, C.S., & Pickett, A. (2006). A study of teaching presence and student sense of learning community in fully online and web-enhanced college courses. Internet & Higher Education, 9(3), 175–190.SheaP.LiC.S.PickettA.2006A study of teaching presence and student sense of learning community in fully online and web-enhanced college coursesInternet & Higher Education9317519010.1016/j.iheduc.2006.06.005Search in Google Scholar

Turney, P.D., & Littman, M.L. (2003). Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems, 21(4), 315–346.TurneyP.D.LittmanM.L.2003Measuring praise and criticism: Inference of semantic orientation from associationACM Transactions on Information Systems21431534610.1145/944012.944013Search in Google Scholar

Vinodhini, G. (2014). Sentiment mining using SVM-based hybrid classification model. Advances in Intelligent Systems & Computing, 246, 155–162.VinodhiniG.2014Sentiment mining using SVM-based hybrid classification modelAdvances in Intelligent Systems & Computing24615516210.1007/978-81-322-1680-3_18Search in Google Scholar

Wang, J., Zuo, W., & Tao, P. (2015). Hyponymy graph model for word semantic similarity measurement. Chinese Journal of Electronics, 24(1), 96–101.WangJ.ZuoW.TaoP.2015Hyponymy graph model for word semantic similarity measurementChinese Journal of Electronics2419610110.1049/cje.2015.01.016Search in Google Scholar

Wang, K., Pan, W., & Yang, B.H. (2019). Analysis of topic emotion evolution based on OTSRM model. Journal of Information, 38(05), 534–542.WangK.PanW.YangB.H.2019Analysis of topic emotion evolution based on OTSRM modelJournal of Information3805534542Search in Google Scholar

Wei, L., Wang, Z., Qian, T., & Wan Q. (2019). Attribute reduction in the background of multi-source decision forms. Journal of Shaanxi Normal University (Natural Science Edition), 47(5), 57–63.WeiL.WangZ.QianT.WanQ.2019Attribute reduction in the background of multi-source decision formsJournal of Shaanxi Normal University (Natural Science Edition)4755763Search in Google Scholar

Wu, H.C., Luk, R.W.P., Wong, K.F., & Kwok, K.L. (2008). Interpreting TF-IDF term weights as making relevance decisions. Acm Transactions on Information Systems, 26(3), 55–59.WuH.C.LukR.W.P.WongK.F.KwokK.L.2008Interpreting TF-IDF term weights as making relevance decisionsAcm Transactions on Information Systems263555910.1145/1361684.1361686Search in Google Scholar

Wu, W.C.V., Hsieh, J.S.C., & Yang, J.C. (2017). Creating an online learning community in a flipped classroom to enhance EFL learners’ oral proficiency. Journal of Educational Technology & Society, 20(2), 142–157.WuW.C.V.HsiehJ.S.C.YangJ.C.2017Creating an online learning community in a flipped classroom to enhance EFL learners’ oral proficiencyJournal of Educational Technology & Society202142157Search in Google Scholar

Xie, X., Ge, S., Hu, F., Xie, M., & Jiang, N. (2017). An improved algorithm for sentiment analysis based on maximum entropy. Soft Computing, 23(1), 599–611.XieX.GeS.HuF.XieM.JiangN.2017An improved algorithm for sentiment analysis based on maximum entropySoft Computing23159961110.1007/s00500-017-2904-0Search in Google Scholar

Yang, M., Peng, B.L., & Chen, Z. (2014). A topic model for building fine-grained domain-specific emotion lexicon. Association for Computational Linguistics (ACL), pp 421–426.YangM.PengB.L.ChenZ.2014A topic model for building fine-grained domain-specific emotion lexiconAssociation for Computational Linguistics (ACL)42142610.3115/v1/P14-2069Search in Google Scholar

Yang, C., Zhang, H., & Shi, D. (2014). An on-line adaptive topic evolution model in web discussions. IEEE International Conference on Fuzzy Systems & Knowledge Discovery. July 23–25, Shenyang, China. pp 116–124.YangC.ZhangH.ShiD.2014An on-line adaptive topic evolution model in web discussionsIEEE International Conference on Fuzzy Systems & Knowledge DiscoveryJuly 23–25Shenyang, China116124Search in Google Scholar

Yue, G., Barnes, S.J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management, 59, 467–483.YueG.BarnesS.J.JiaQ.2017Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocationTourism Management5946748310.1016/j.tourman.2016.09.009Search in Google Scholar

Zhang, Y.F., Li, H., Peng, L.H., & Hou, L.T. (2017). The usefulness classification algorithm and application of online reviews based on emotional semantic feature extraction. Data Analysis and Knowledge Discovery, 1(12), 74–83.ZhangY.F.LiH.PengL.H.HouL.T.2017The usefulness classification algorithm and application of online reviews based on emotional semantic feature extractionData Analysis and Knowledge Discovery1127483Search in Google Scholar

Zhang, W.X., Wei, L., & Qi, J.J. (2005). The theory and method of attribute reduction of concept lattice. Chinese Science E Series: Information Science, 6, 628–639.ZhangW.X.WeiL.QiJ.J.2005The theory and method of attribute reduction of concept latticeChinese Science E Series: Information Science6628639Search in Google Scholar

Zhao, Y., Qin, B., Liu, T., & Tang, D. (2016). Social sentiment sensor: A visualization system for topic detection and topic sentiment analysis on microblog. Multimedia Tools and Applications, 75(15), 8843–8860.ZhaoY.QinB.LiuT.TangD.2016Social sentiment sensor: A visualization system for topic detection and topic sentiment analysis on microblogMultimedia Tools and Applications75158843886010.1007/s11042-014-2184-ySearch in Google Scholar

Zhi, X. (2002). Realization and optimization of association rule mining algorithm. Computer Engineering & Applications, 6(4), 341–357.ZhiX.2002Realization and optimization of association rule mining algorithmComputer Engineering & Applications6434135710.3233/IDA-2002-6404Search in Google Scholar

Zheng, Q., Lu, Z., Yang, W., Zhang, M., Feng, Q., & Chen, W. (2013). A robust medical image segmentation method using KL distance and local neighborhood information. Computers in Biology & Medicine, 43(5), 459–470.ZhengQ.LuZ.YangW.ZhangM.FengQ.ChenW.2013A robust medical image segmentation method using KL distance and local neighborhood informationComputers in Biology & Medicine43545947010.1016/j.compbiomed.2013.01.00223566392Search in Google Scholar

Zhong, J., Zhang, S.F., Guo, W.L., & Li, X. (2018). TFLA: A Quality Analysis framework for student Generated contents. Acta Electronica Sinica, 46(9), 2201–2206.ZhongJ.ZhangS.F.GuoW.L.LiX.2018TFLA: A Quality Analysis framework for student Generated contentsActa Electronica Sinica46922012206Search in Google Scholar

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