Ontology-based sentimental knowledge in predicting the product recommendations: A data science approach

References

• Santosh, D. Teja, and B. Vishnu Vardhan. “Automatic Machine Recognition Of Features And Sentiments From Online Reviews.” IAENG TRANSACTIONS ON ENGINEERING SCIENCES: Special Issue for the International Association of Engineers Conferences 2015. 2017.
   Google Scholar
• Dong, Ruihai, et al. “Opinionated product recommendation.” International conference on case-based reasoning. Springer, Berlin, Heidelberg, (2013).
   Google Scholar
• Dong, Ruihai, et al. “Combining similarity and sentiment in opinion mining for product recommendation.” Journal of Intelligent Information Systems 46.2 (2016): 285-312. doi: 10.1007/s10844-015-0379-y
   Web of Science ®Google Scholar
• Burke, Robin. “Hybrid recommender systems: Survey and experiments.” User modeling and user-adapted interaction 12.4 (2002): 331-370. doi: 10.1023/A:1021240730564
   Web of Science ®Google Scholar
• Resnick, Paul, et al. “GroupLens: an open architecture for collaborative filtering of netnews.” Proceedings of the 1994 ACM conference on Computer supported cooperative work. ACM, 1994.
   Google Scholar
• Stavrianou, Anna, and Caroline Brun. “Expert Recommendations Based on Opinion Mining of User-Generated Product Reviews.” Computational Intelligence 31.1 (2015): 165-183. doi: 10.1111/coin.12021
   Web of Science ®Google Scholar
• Lang, Ken. “Newsweeder: Learning to filter netnews.” Machine Learning Proceedings 1995. Morgan Kaufmann, 1995. 331-339.
   Google Scholar
• Zhou, Jia, Tiejian Luo, and Fuxing Cheng. “Modeling learners and contents in academic-oriented recommendation framework.” 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing. IEEE, 2011.
   Google Scholar
• Kolodner, Janet. Case-based reasoning. Morgan Kaufmann, 2014.
   Google Scholar
• Burke, Robin D., Kristian J. Hammond, and B. C. Yound. “The Find-Me approach to assisted browsing.” IEEE Expert 12.4 (1997): 32-40. doi: 10.1109/64.608186
   Google Scholar
• Holland, Stefan, Martin Ester, and Werner Kießling. “Preference mining: A novel approach on mining user preferences for personalized applications.” European Conference on Principles of Data Mining and Knowledge Discovery. Springer, Berlin, Heidelberg, 2003.
   Google Scholar
• Yaakub, Mohd Ridzwan, Yuefeng Li, and Jinglan Zhang. “Integration of sentiment analysis into customer relational model: the importance of feature ontology and synonym.” Procedia Technology 11 (2013): 495-501. doi: 10.1016/j.protcy.2013.12.220
   Google Scholar
• Agarwal, Basant, et al. “Sentiment analysis using common-sense and context information.” Computational intelligence and neuroscience 2015 (2015): 30.
   Google Scholar
• Chen, Guanliang, and Li Chen. “Recommendation based on contextual opinions.” International Conference on User Modeling, Adaptation, and Personalization. Springer, Cham, 2014.
   Google Scholar
• Gurini, Davide Feltoni, et al. “Analysis of Sentiment Communities in Online Networks.” SPS@ SIGIR. 2015.
   Google Scholar
• Li, Xiu, Huimin Wang, and Xinwei Yan. “Accurate recommendation based on opinion mining.” Genetic and Evolutionary Computing. Springer, Cham, 2015. 399-408.
   Google Scholar
• Uzun, Abdulbaki, and Christian Räck. “Exploiting ontologies for better recommendations.” INFORMATIK 2010. Service Science–Neue Perspektiven für die Informatik. Band 1 (2010).
   Google Scholar
• Farsani, Hadi Khosravi, and Mohamad Ali Nematbakhsh. “Designing A Catalog Management System–An Ontology Approach.” Malaysian Journal of Computer Science 20.1 (2007): 35. doi: 10.22452/mjcs.vol20no1.4
   Google Scholar
• Santosh, D. Teja, and B. Vishnu Vardhan. “PROO ontology development for learning feature specific sentiment relationship rules on reviews categorisation: a semantic data mining approach.” International Journal of Metadata, Semantics and Ontologies 11.1 (2016): 29-38. doi: 10.1504/IJMSO.2016.078105
   Google Scholar
• Waqar, Muhammad, et al. “An adaptive doctor-recommender system.” Behaviour & Information Technology (2019): 1-15.
   Google Scholar
• Katarya, Rahul. “Reliable Recommender System Using Improved Collaborative Filtering Technique.” System Reliability Management: Solutions and Technologies (2018): 113. doi: 10.1201/9781351117661-8
   Google Scholar
• Davenport, Thomas H. “From analytics to artificial intelligence.” Journal of Business Analytics 1.2 (2018): 73-80. doi: 10.1080/2573234X.2018.1543535
   Google Scholar
• Manning, Christopher D. “Part-of-speech tagging from 97% to 100%: is it time for some linguistics?.” International Conference on Intelligent Text Processing and Computational Linguistics. Springer Berlin Heidelberg, 2011.
   Google Scholar
• Toutanova, Kristina, and Christopher D. Manning. “Enriching the knowledge sources used in a maximum entropy part-of-speech tagger.” Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics-Volume 13. Association for Computational Linguistics, 2000.
   Google Scholar
• Liu, Bing. Web data mining: exploring hyperlinks, contents, and usage data. Springer Science & Business Media, 2007.
   Google Scholar
• Pang, Bo, Lillian Lee, and Shivakumar Vaithyanathan. “Thumbs up?: sentiment classification using machine learning techniques.” Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10. Association for Computational Linguistics, 2002.
   Google Scholar
• Santosh, D. Teja, and B. Vishnu Vardhan. “Obtaining feature-and sentiment-based linked instance RDF data from unstructured reviews using ontology-based machine learning.” International Journal of Technology 2 (2015): 198-206.
   Google Scholar
• Hu, Minqing, and Bing Liu. “Mining opinion features in customer reviews.” AAAI. Vol. 4. No. 4. 2004.
   Google Scholar
• Baccianella, Stefano, Andrea Esuli, and Fabrizio Sebastiani. “Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining.” Lrec. Vol. 10. No. 2010. 2010.
   Google Scholar
• Huang, Anna. “Similarity measures for text document clustering.” Proceedings of the sixth new zealand computer science research student conference (NZCSRSC2008), Christchurch, New Zealand. Vol. 4. 2008.
   Google Scholar
• Alahmadi, Dimah, and Xaio-Jun Zeng. “Improving recommendation using trust and sentiment inference from osns.” International Journal of Knowledge Engineering 1.1 (2015): 9-17. doi: 10.7763/IJKE.2015.V1.2
   Google Scholar
• Kumar, KC Ravi, D. Teja Santosh, and B. Vishnu Vardhan. “Determining the semantic orientation of opinion words using typed dependencies for opinion word senses and SentiWordNet scores from online product.” (2017). doi: 10.1504/IJKWI.2017.10010171.
   Google Scholar
• Zhang, Hong, Ling Zhao, and Sumeet Gupta. “The role of online product recommendations on customer decision making and loyalty in social shopping communities.” International Journal of Information Management 38.1 (2018): 150-166. doi: 10.1016/j.ijinfomgt.2017.07.006
   Web of Science ®Google Scholar
• Singh, Pradeep Kumar, Pijush Kanti Dutta Pramanik, and Prasenjit Choudhury. “An improved similarity calculation method for collaborative filtering-based recommendation, considering neighbor’s liking and disliking of categorical attributes of items.” Journal of Information and Optimization Sciences 40.2 (2019): 397-412. doi: 10.1080/02522667.2019.1580881
   Web of Science ®Google Scholar
• Yang, Fan. “A hybrid recommendation algorithm–based intelligent business recommendation system.” Journal of Discrete Mathematical Sciences and Cryptography 21.6 (2018): 1317-1322. doi: 10.1080/09720529.2018.1526408
   Web of Science ®Google Scholar