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IJSTR >> Volume 8 - Issue 11, November 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Opinion Analysis For Educational Field

[Full Text]



Sanjay Singh Bhadoria



Support Vector Machines (SVM), Neural Networks, Decision Tree, Naïve Bayes, Sentiment analysis



Opinion mining is an important area of research in the recent years which combines web mining with computational intelligence to collect opinions through websites, social media, company data analysis and customers. Opinion mining algorithms collect opinions from websites and classify them using the mining process such as Support Vector Machines (SVM), Neural Networks, Decision Tree, Naïve Bayes and other classifiers. Moreover, opinion mining is useful in business since it highlights the positive or negative attitude of their students as well as the products and services. This helps the business managers to improve their method of services and to modify the products which will suit the student interests. Sentiment analysis is a type of opinion mining technique which uses natural language processing and other computational intelligence techniques to make effective decisions.



[1] Abbasi, A, Chen, H & Salem, A 2008, ‘Sentiment analysis in multiplelanguages: Feature selection for opinion classification in web forums’,ACM Transactions on Information Systems, vol. 26.
[2] Abdullah Uz Tansel, James Clifford, Gadia, S, Sushil Jajodia, Arie
[3] Segav & Snodgrass, R 1993, ‘Temporal Databases, Theory, Designand Implementation’, The Benjamin / Cummings Publishing Company Inc.[Adomavicius, G, Sankaranarayanan, R, Sen, S & Tuzhilin, A 2005,Incorporating contextual information in recommender systems using amultidimensional approach’, ACM Trans. Inf.Syst., vol. 23, no.1
[4] Agrawal, R & Srikant, R 1995, ‘Mining Sequential Patterns’,Proceedings of 11th International Conference on Data Engineering,
[5] IEEE Computer Society Press.Bridge, D, Goker, M, McGinty, L & Smyth, B 2006, ‘Case-basedrecommender systems’, The Knowledge Engineering Review, vol. 20,no. 3.
[6] Carlos Bobed & Eduardo Mena 2016, ‘QueryGen: SemanticInterpretation of Keyword Queries Over Heterogeneous InformationSystems’, Information Sciences, vol.329.
[7] [Chupin Chao & Wenbao Jiang 2012, ‘Study on the Subjective andObjective Text Classification and Pretreatment of Chinese NetworkText’, 4th International Conference on Intelligent Human MachineSystems and Cybernetics.
[8] Cooley, R, Mobasher, B & Srivastava, J 1999, ‘Data Preparation forMining World Wide Web Browsing Patterns’, Journal of Knowledge and Information Systems, vol. 1, no.1.
[9] Ganapathy, S, Kulothungan, K, Yogesh, P & Kannan, A 2012, ‘ANovel Weighted Fuzzy C –Means Clustering Based on ImmuneGenetic Algorithm for Intrusion Detection’, Procedia EngineeringJournal – Elsevier, vol. 38.
[10] Garofalakis, MN, Rastogi, R, Seshadri, S & Shim, K 1999, ‘DataMining and the Web: Past, Present and Future’, Proceedings of the 2ndACM International Conference on Web Information and Data Management.
[11] Kavita Ganesan & Cheng Xiang Zhai 2011, ‘Opinion-Based Entity Ranking’, Information Retrieval.
[12] [Khan, A, Baharudin, B & Khan, K 2011, ‘Sentiment Classificationfrom Online Customer Reviews Using Lexical Contextual Sentence
[13] Structure’, ICSECS 2011: 2nd International Conference on Software
[14] Engineering and Computer Systems, Springer.