NLP , ML , Algorithms Addressing Challenges of Opinion Mining in Businesses

M.B. Nasreen Taj & G.S. Girisha, Insights of strength and weakness of evolving methodologies of sentiment

analysis, Global Transitions Proceedings 2 (2021) 157-162. http://www.keaipublishing.com/en/journals/global-transitions-proceedings/

Prajna. P & Anubha. R, Opinion Mining: A Communication Tool For Career Making, Gedrag & Organisatie Review - Issn:0921-5077, Volume 33 : Issue 02 - 2020.

Pravesh Kumar Singh & Mohd Shahid Husain, METHODOLOGICAL STUDY OF OPINION MINING AND SENTIMENT ANALYSIS TECHNIQUES, International Journal on Soft Computing (IJSC) Vol. 5, No. 1, February 2014.

Kumar Ravi and Vadlamani Ravi,A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-Based Systems 89 (2015) 14–46

http://dx.doi.org/10.1016/j.knosys.2015.06.015, June 2015.

Jasleen Kaur and Dr.Jatinder Kumar R. Saini1 , An Analysis of Opinion Mining Research Works Based on Language, Writing Style and Feature Selection Parameters, International Journal of Advanced Networking Applications (IJANA) ISSN No. : 0975-0290 November 2014.

Harshi Garg and Niranjan Lal, Data Analysis: Opinion Mining and Sentiment Analysis of Opinionated Unstructured Data, © Springer Nature Singapore Pte Ltd. 2018 M. Singh et al. (Eds.): ICACDS 2018, CCIS 906, pp. 249–258,2018.https://doi.org/10.1007/978-981-13-1813-9_25.

Elham Asani a, Hamed Vahdat-Nejad a & Javad Sadri,

Restaurant recommender system based on sentiment analysis ,Machine Learning with Applications 6 (2021) 100114- journal homepage: www.elsevier.com/locate/mlwa https://doi.org/10.1016/j.mlwa.2021.100114,

Ana Reyes-Menendez, Jose Ramon Saura & Ferrao Filipe, Marketing challenges in the #MeToo era: gaining business insights using an exploratory sentiment analysis, https://doi.org/10.1016/j.heliyon.2020.e03626,September 2019;2405-8440/© 2020 Published by Elsevier Ltd.

Walaa Medhat, Ahmed Hassan & Hoda Korashy, Sentiment analysis algorithms and applications: A survey, Ain Shams Engineering Journal (2014) 5,1093-1113, www.elsevier.com/locate/asej, www.sciencedirect.com, 2090-4479 2014 Production and hosting by Elsevier B.V. on behalf of Ain Shams University. http://dx.doi.org/10.1016/j.asej.2014.04.011

Ravinder Ahujaa, Aakarsha Chuga, Shruti Kohlia, Shaurya Guptaa and Pratyush Ahujaa, The Impact of Features Extraction on the Sentiment Analysis, Procedia Computer Science 152 (2019) 341–348, www.sciencedirect.com

Akilandeswari Ja & Jothi Gb, Sentiment Classification of Tweets with Non-Language Features, Procedia Computer Science 143 (2018) 426–433, www.sciencedirect.com, 8th International Conference on Advances in Computing and Communication (ICACC-2018)

Ghadah Alwakid, Taha Osman and Thomas Hughes-Roberts , Challenges in Sentiment Analysis for Arabic Social Networks, 3rd International Conference on Arabic Computational Linguistics, ACLing 2017, 5-6 November 2017, Dubai, United Arab Emirates, Procedia Computer Science 117 (2017) 89–100

Abinash Tripathya, Ankit Agrawalb & Santanu Kumar Rath, Classification of Sentimental Reviews Using Machine Learning Techniques, 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015)

doi: 10.1016/j.procs.2015.07.523,Procedia Computer Science 57 ( 2015 ) 821 – 829, www.sciencedirect.com, 1877-0509 © 2015

Doaa Mohey El-Din Mohamed Hussein, A survey on sentiment analysis challenges,Journal of King Saud University – Engineering Sciences (2018) 30, 330-338 ,www.ksu.edu.sa, www.sciencedirect.com

Khairullah Khan, Baharum Baharudin , Aurnagzeb Khan & Ashraf Ullah, Mining opinion components from unstructured reviews: A review, Journal of King Saud University – Computer and Information Sciences (2014) 26, 258-275.www.ksu.edu.sa , www.sciencedirect.com

Rushabh Shah and Bhoomit Patel, Procedure of Opinion Mining and Sentiment Analysis: A Study, International Journal of Current Engineering and Technology, E-ISSN 2277 – 4106, P-ISSN 2347 - 5161 ©2014 INPRESSCO.

Emma Haddia, Xiaohui Liua, Yong Shib (2013)The Role of Text Pre-processing in Sentiment Analysis. Procedia Computer Science 17, 26 – 32.

Mita K. Dalal andMukesh A. Zaveri (2013) Semi Supervised Learning Based Opinion Summarization and Classification for Online Product Reviews. Hindawi Publishing Corporation, Applied Computational Intelligence and Soft Computing, Article ID 910706.

Walaa Medhat , Ahmed Hassan , Hoda Korashy (2014)

Sentiment analysis algorithms and applications:A survey. Ain Shams Engineering Journal.

Lalita Sharma, Shweta Shukla. Classification of Web Blog Mining for Movie Review. International Journal of

Engineering and Advanced Technology (IJEAT) ISSN: 2249 –8958.

Meishan Hu, Aixin Sun and Ee-Peng Lim (2007) Comments-oriented blog summarization by sentence-extraction. In Proceedings of the ACM SIGIR Conference on Information and Knowledge Management(CIKM), 901-904,. ISBN 78-1- 59593-803-9, Post paper.

Charlotta Engstrom (2004) Topic dependence in sentiment classification. Master‟s thesis, University of Cambridge.

Soo-Min Kim and Eduard Hovy (2004) Determining the

sentiment of opinions. In Proceedings of the International

Conference on Computational Linguistics (COLING).

Abbasi, “Affect intensity analysis of dark web forums,” in Proceedings of Intelligence and Security Informatics (ISI), pp. 282–288, 2007.

K. Dave, S. Lawrence & D. Pennock. Mining the Peanut Gallery: Opinion Extraction and Semantic Classi_cation of Product Reviews." Proceedings of the 12th International Conference on World Wide Web, pp. 519-528, 2003.

B. Liu. Web Data Mining: Exploring hyperlinks, contents, and usage data," Opinion Mining. Springer, 2007.

B. Pang & L. Lee, Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales." Proceedings of the Association for Computational Linguistics (ACL), pp. 15124,2005.

Nilesh M. Shelke, Shriniwas Deshpande, Vilas Thakre, Survey of Techniques for Opinion Mining, International Journal of Computer Applications (0975 – 8887) Volume 57– No.13, November 2012.

Ion SMEUREANU, Cristian BUCUR, Applying Supervised Opinion Mining Techniques on Online

User Reviews, Informatica Economică vol. 16, no. 2/2012.

Bo Pang and Lillian Lee, “Opinion Mining and Sentiment Analysis”, Foundations and TrendsR_ in

Information Retrieval Vol. 2, Nos. 1–2 (2008).

Vaghela, V.B., Jadav, B.M.: Analysis of various sentiment classification techniques. Int.J. Comput. Appl. 140, 1–6 (2016)

Hamzah, A., Widyastuti, N.: Opinion classification using maximum entropy and K-means clustering. In: International Conference on Information, Communication Technology and System, IEEE International Conference, pp. 162–166 (2016)

Songpan, W.: The analysis and prediction of customer review rating using opinion mining.In: IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA), pp. 71–77 (2017)

Hailong, Z., Wenyan, G., Bo, J.: Machine learning and lexicon based methods for sentiment classification: a survey. In: IEEE International Conference on Web Information System and Application (WISA), pp. 262–265 (2014)

Guellil, I., Boukhalfa, K.: Social big data mining: a survey focused on opinion mining and sentiments analysis. In: 12th International Symposium on Programming and Systems (ISPS). IEEE (2015)

Osimo, D., Mureddu, F.: Research challenge on opinion mining and sentiment analysis. In: The CROSSROAD Roadmap on ICT for Governance and Policy Modeling (2010)

Vo, A.-D., Nguyen, Q.-P., Ock, C.-Y.: Opinion—aspect relations in cognizing customer feelings via reviews. In: IEEE, pp. 5415–5426 (2018)

Denecke, K. (2008). Using sentiwordnet for multilingual sentiment analysis. In IEEE 24th international conference on data engineering workshop. Cancun, Mexico.

Esmaeili, L., Mardani, S., Hashemi Golpayegani, S. A., & Zanganeh Madar, Z. (2020). A novel tourism recommender system in the context of social commerce. Expert Systems with Applications, 149(1), A novel tourism recommender system in the context of social commerce.

García-Sánchez, F., Colomo-Palacios, R & alencia-García, R. (2020). A social-semantic recommender system for advertisements. Information Processing & Management,57(2), Article 102153.

Ghiassi, M., & Lee, S. (2018). A domain transferable lexicon set for Twitter senti- ment analysis using a supervised machine learning approach. Expert Systems with

Applications, 106(1), 197–216.

Hedge, S. B., Satyappanavar, S., & Setty, S. (2018). Sentiment based food classification for restaurant business. In International conference on advances in computing, communications and informatics (pp. 1455–1462). Bangalore, India: IEEE.