An Intensive Spectrum for Intention Mining Analysis

Main Article Content

Varsha D. Jadhav, Dhananjay R. Dolas, Nakul Sharma, Amar Buchade, Mandar Diwakar

Abstract

There is huge volume of data in the social networks. This data can be retrieved and integrated to extract useful meaning and come out with the insights which is called as intentions. This can be used in different fields like business, recommender systems, education, Scientific research, games, etc. Also, there are various intention mining techniques which can be applied to several fields as information retrieval, business, etc. There is no specific definition of intention mining and also there is very less existing literature present. Accordingly, there is need to conduct systematic literature review of the very recent research area. Understanding intention mining, purpose of intention mining, categories and techniques of intention mining is the need. The paper endorses a spectrum for intention mining so that further literature review of intention mining can be completed. We validate our work through dimensions, categories and techniques for intention mining.

Article Details

How to Cite
Varsha D. Jadhav, et al. (2023). An Intensive Spectrum for Intention Mining Analysis. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 83–90. https://doi.org/10.17762/ijritcc.v11i10.8468
Section
Articles
Author Biography

Varsha D. Jadhav, Dhananjay R. Dolas, Nakul Sharma, Amar Buchade, Mandar Diwakar

Varsha D. Jadhav1, Dhananjay R. Dolas2, Nakul Sharma3, Amar Buchade4, Mandar Diwakar5

1Artificial Intelligence and Data Science Department

Vishwakarma Institute of Information Technology

Pune, Maharashtra, India

e-mail: drvarshajadhav22@gmail.com

2Mechanical Engineering Department

Jawaharlal Nehru Engineering College, MGM University

Aurangabad,Maharashtra,India

e-mail: drdolasjnec@gmail.com.

3Artificial Intelligence and Data Science Department

Vishwakarma Institute of Information Technology

Pune, Maharashtra, India

e-mail: nakul777@gmail.com

4Artificial Intelligence and Data Science Department

Vishwakarma Institute of Information Technology

Pune, Maharashtra, India

e-mail: amar.buchade@viit.ac.in

5Artificial Intelligence and Data Science Department

Vishwakarma Institute of Information Technology

Pune, Maharashtra, India

e-mail: mpdiwakar30@gmail.com

References

Bing Liu, “Opinions, Sentiment, and Emotion in Text,” Cambridge University Press, 04-Jun-2015.

Hall, Peter. (2016). Intention. 10.1007/978-1-4614-6439-6_1697-2.

Pacherie, Elisabeth & Haggard, Patrick. (2010). What Are Intentions?. Conscious Will and Responsibility: A Tribute to Benjamin Libet. 10.1093/acprof:oso/9780195381641.003.0008.

Yoon S, Elhadad N, Bakken S. A practical approach for content mining of Tweets. Am J Prev Med. 2013 Jul;45(1):122-129. doi: 10.1016/j.amepre.2013.02.025. PMID: 23790998; PMCID: PMC3694275.

Khodabandelou, G., Hug, C., Deneckere, R., Salinesi, C., 2013b. Process mining versus intention mining, in: Enterprise, Business-Process and Information Systems Modeling. Springer, pp. 466–480.

D. Birant, Ed., Data Mining - Methods, Applications and Systems. IntechOpen, 2021. doi: 10.5772/intechopen.87784.

Diaz-Rodriguez, Oswaldo E. & Perez, Maria & Lascano, Jorge. (2019). Literature Review about Intention Mining in Information Systems. Journal of Computer Information Systems. 61. 1-10. 10.1080/08874417.2019.1633569.

Bouricha, H., Hsairi, L. & Ghédira, K. Literature review on Intention Mining-oriented Process Mining in information system. Artif Intell Rev (2023). https://doi.org/10.1007/s10462-023-10490-8

Bouricha Hajer, Benlashram Arwa, Hsairi Lobna, Ghedira Khaled, Intention Mining Data preprocessing based on Multi-Agents System,Procedia Computer Science,Volume 176,2020, Pages 888-897, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2020.09.084.

A. Habib, F. K. Saddozai, A. Sattar, A. Khan, I. A. Hameed and F. M. Kundi, "User Intention Mining in Bussiness Reviews: A Review," 2018 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC), Kaohsiung, Taiwan, 2018, pp. 243-249, doi: 10.1109/BESC.2018.8697303.

A. Razia Sulthana, Subburaj Ramasamy,Ontology and context based recommendation system using Neuro-Fuzzy Classification,Computers & Electrical Engineering, Volume 74, 2019, Pages 498-510, ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2018.01.034

Aleksandra Karpus; Iacopo Vagliano; Krzysztof Goczy?a;Maurizio Morisio; An Ontology-based Contextual Pre-filtering Technique for Recommender Systems, Proceedings of the Federated Conference on Computer Science and Information Systems pp. 411–420, ACSIS, Vol. 8. ISSN 2300-5963, DOI: 10.15439/2016F428

Jinpeng Wang, Gao Cong, Wayne Xin Zhao, and Xiaoming Li. 2015. Mining user intents in twitter: a semi-supervised approach to inferring intent categories for tweets. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI'15). AAAI Press, 318–324

Symeon Symeonidis & Georgios Peikos & Avi Arampatzis, 2022. "Unsupervised consumer intention and sentiment mining from microblogging data as a business intelligence tool," Operational Research, Springer, vol. 22(5), pages 6007-6036, November.

Markus Strohmaier and Mark Kröll. 2012. Acquiring knowledge about human goals from Search Query Logs. Inf. Process. Manage. 48, 1 (January, 2012), 63–82. https://doi.org/10.1016/j.ipm.2011.03.010

Jyun-Yu Jiang, Chia-Jung Lee, Longqi Yang, Bahareh Sarrafzadeh, Brent Hecht, and Jaime Teevan. 2021. Learning to Represent Human Motives for Goal-directed Web Browsing. In Proceedings of the 15th ACM Conference on Recommender Systems (RecSys '21). Association for Computing Machinery, New York, NY, USA, 361–371. https://doi.org/10.1145/3460231.3474260

Korotaev, Sergey and Gasiukova, Elena and Karacharovskiy, Vladimir, Human Goals: Introducing Agency in Social Distinction Studies (April 12, 2022). Available at SSRN: https://ssrn.com/abstract=4082198 or http://dx.doi.org/10.2139/ssrn.4082198

A. Ashkan, C. L. A. Clarke, E. Agichtein and Q. Guo, "Estimating Ad Clickthrough Rate through Query Intent Analysis," 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Milan, Italy, 2009, pp. 222-229, doi: 10.1109/WI-IAT.2009.39.

Ashkan, A., Clarke, C.L.A. Impact of query intent and search context on clickthrough behavior in sponsored search. Knowl Inf Syst 34, 425–452 (2013). https://doi.org/10.1007/s10115-012-0485-x

Varsha D. Jadhav and Sachin N. Deshmukh. 2017. Twitter Intention Classification Using Bayes Approach for Cricket Test Match Played Between India and South Africa 2015. Int. J. Rough Sets Data Anal. 4, 2 (April 2017), 49–62. https://doi.org/10.4018/IJRSDA.2017040104

F. A. H. Ambreen and V. D. Jadhav, "Novel Model for Detection of Cyber-Aggressive Comments on Social Media Platforms a Review," 2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC), Aurangabad, India, 2020, pp. 118-121, doi: 10.1109/ICSIDEMPC49020.2020.9299608.

Schröder, T., Stewart, T.C. and Thagard, P. (2014), Intention, Emotion, and Action: A Neural Theory Based on Semantic Pointers. Cogn Sci, 38: 851-880. https://doi.org/10.1111/cogs.12100

Viebahn, E. (2020), Ways of Using Words: On Semantic Intentions. Philos Phenomenol Res, 100: 93-117. https://doi.org/10.1111/phpr.12526

B. Barla Cambazoglu, Leila Tavakoli, Falk Scholer, Mark Sanderson, and Bruce Croft. 2021. An Intent Taxonomy for Questions Asked in Web Search. In Proceedings of the 2021 Conference on Human Information Interaction and Retrieval (CHIIR '21). Association for Computing Machinery, New York, NY, USA, 85–94. https://doi.org/10.1145/3406522.3446027

Long Chen, Dell Zhang, and Levene Mark. 2012. Understanding user intent in community question answering. In Proceedings of the 21st International Conference on World Wide Web (WWW '12 Companion). Association for Computing Machinery, New York, NY, USA, 823–828. https://doi.org/10.1145/2187980.2188206

Dincer, Caner, and Banu Dincer. 2023. "Social Commerce and Purchase Intention: A Brief Look at the Last Decade by Bibliometrics" Sustainability 15, no. 1: 846. https://doi.org/10.3390/su15010846

Rodrigo Uribe, Cristian Buzeta, Milenka Velásquez, Sidedness, commercial intent and expertise in blog advertising, Journal of Business Research, Volume 69, Issue 10, 2016, Pages 4403-4410, ISSN 0148-2963, https://doi.org/10.1016/j.jbusres.2016.04.102

TUOMELA, R. (2006), Joint Intention, We-Mode and I-Mode. Midwest Studies In Philosophy, 30: 35-58. https://doi.org/10.1111/j.1475-4975.2006.00127.x

Kibler, Ewald. (2013). Formation of Entrepreneurial Intentions in a Regional Context. Entrepreneurship and Regional Development. 25. 293-323. 10.1080/08985626.2012.721008.

Y. Wei et al., "Predicting Entrepreneurial Intention of Students: An Extreme Learning Machine With Gaussian Barebone Harris Hawks Optimizer," in IEEE Access, vol. 8, pp. 76841-76855, 2020, doi: 10.1109/ACCESS.2020.2982796.

G. Khodabandelou, C. Hug and C. Salinesi, "A novel approach to process mining: Intentional process models discovery," 2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS), Marrakech, Morocco, 2014, pp. 1-12, doi: 10.1109/RCIS.2014.6861040.

Esmaeili, Leila, and Alireza Hashemi Golpayegani. 2021. "A Novel Method for Discovering Process Based on the Network Analysis Approach in the Context of Social Commerce Systems" Journal of Theoretical and Applied Electronic Commerce Research 16, no. 2: 34-62. https://doi.org/10.4067/S0718-18762021000200104

Espinoza, M.M., Possebom, A.T., Puyol-Gruart, J., & Tacla, C.A. (2019). Argumentation-based Intention Formation Process. DYNA. https://doi.org/10.15446/dyna.v86n208.66597

Imran AS, Daudpota SM, Kastrati Z, Batra R. Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets. IEEE Access. 2020 Sep 28;8:181074-181090. doi: 10.1109/ACCESS.2020.3027350. PMID: 34812358; PMCID: PMC8545282.

Ullah, Farhat, Xin Chen, Syed Bilal Hussain Shah, Saoucene Mahfoudh, Muhammad Abul Hassan, and Nagham Saeed. 2022. "A Novel Approach for Emotion Detection and Sentiment Analysis for Low Resource Urdu Language Based on CNN-LSTM" Electronics 11, no. 24: 4096. https://doi.org/10.3390/electronics11244096

Diaz, Gerardo & Ng, Vincent. (2020). Unveiling Hidden Intentions. Proceedings of the AAAI Conference on Artificial Intelligence. 34. 13550-13555. 10.1609/aaai.v34i09.7080.

Rashid, A., Farooq, M.S., Abid, A. et al. Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challenges. Complex Intell. Syst. 9, 2773–2799 (2023). https://doi.org/10.1007/s40747-021-00342-9

Ghabayen, Ayman S. and Ahmed, Basem H.. "Polarity Analysis of Customer Reviews Based on Part-of-Speech Subcategory" Journal of Intelligent Systems, vol. 29, no. 1, 2020, pp. 1535-1544. https://doi.org/10.1515/jisys-2018-0356

Nandwani, Pansy & Verma, Rupali. (2021). A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining. 11. 10.1007/s13278-021-00776-6.

L. Nahar, Z. Sultana, N. Iqbal and A. Chowdhury, "Sentiment Analysis and Emotion Extraction: A Review of Research Paradigm," 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 2019, pp. 1-8, doi: 10.1109/ICASERT.2019.8934654.

Diaz, Gerardo & Ng, Vincent. (2020). Unveiling Hidden Intentions. Proceedings of the AAAI Conference on Artificial Intelligence. 34. 13550-13555. 10.1609/aaai.v34i09.7080.

Vivekananthamoorthy, Natarajan & Shanmuganathan, Sankar. (2022). Understanding Query Intention in Search Queries of Learners in Blended Learning Environments. 308-315. 10.1109/ICCCMLA56841.2022.9989024.

Jansen, Jim & Booth, Danielle & Spink, Amanda. (2007). Determining the user intent of web search engine queries. 16th International World Wide Web Conference, WWW2007. 1149-1150. 10.1145/1242572.1242739.

Athukorala, Kumaripaba & G?owacka, Dorota & Jacucci, Giulio & Oulasvirta, Antti & Vreeken, Jilles. (2015). Is exploratory search different? A comparison of information search behavior for exploratory and lookup tasks. Journal of the Association for Information Science and Technology. 67. n/a-n/a. 10.1002/asi.23617.

Hanna, Wael & Asem, Aziza & Senousy, M.. (2016). Dynamic Query Intent Prediction from a Search Log Stream. International Journal of Information Retrieval Research. 6. 66-85. 10.4018/IJIRR.2016040104.

Helia Hashemi, Hamed Zamani, and W. Bruce Croft. 2021. Learning Multiple Intent Representations for Search Queries. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM ’21), November 1–5, 2021, Virtual Event, QLD, Australia. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3459637.3482445

Zhang, Na, Ping Yu, Yupeng Li, and Wei Gao. 2022. "Research on the Evolution of Consumers’ Purchase Intention Based on Online Reviews and Opinion Dynamics" Sustainability 14, no. 24: 16510. https://doi.org/10.3390/su142416510

Nathalie Peña-García, Irene Gil-Saura, Augusto Rodríguez-Orejuela, José Ribamar Siqueira-Junior, Purchase intention and purchase behavior online: A cross-cultural approach, Heliyon, Volume 6, Issue 6, 2020, e04284, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2020.e04284.

Wang Lei, Zhang Qi, Wong Philip Pong Weng, Purchase Intention for Green Cars Among Chinese Millennials: Merging the Value–Attitude–Behavior Theory and Theory of Planned Behavior,Frontiers in Psychology.Volume 13,2022, URL=https://www.frontiersin.org/articles/10.3389/fpsyg.2022.786292, DOI=10.3389/fpsyg.2022.786292

Xue Wu, "Prediction of Purchase Intention of High and Middle Potential Users in Luxury Hotels Based on Data Mining", Mobile Information Systems, vol. 2022, Article ID 1646260, 10 pages, 2022. https://doi.org/10.1155/2022/1646260

Doaa Herzallah, Francisco Muñoz-Leiva and Francisco Liebana-Cabanillas, Drivers of purchase intention in Instagram Commerce, Spanish Journal of Marketing - ESIC, vol. 26 no. 2,DOI: https://doi.org/10.1108/SJME-03-2022-0043,ISSN: 2444-9709

Karunarathne, Edirisinghe Arachchige Chaminda Prasanna and Thilini, Weerasinge Asha. "Advertising Value Constructs’ Implication on Purchase Intention: Social Media Advertising" Management Dynamics in the Knowledge Economy, vol.10, no.3, 3922, pp.287-303. https://doi.org/10.2478/mdke-2022-0019

Md. Nekmahmud, Farheen Naz, Haywantee Ramkissoon, Maria Fekete-Farkas, Transforming consumers' intention to purchase green products: Role of social media, Technological Forecasting and Social Change, Volume 185, 2022, 122067, ISSN 0040-1625, https://doi.org/10.1016/j.techfore.2022.122067.

Maffei RM, Dunn K, Zhang J, Hsu CE, Holmes JH. Understanding behavioral intent to participate in shared decision-making in medically uncertain situations. Methods Inf Med. 2012;51(4):301-8. doi: 10.3414/ME11-01-0077. Epub 2012 Jul 20. PMID: 22814528; PMCID: PMC4425218.

Ángel F. Agudo-Peregrina, Ángel Hernández-García, Félix J. Pascual-Miguel, Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning, Computers in Human Behavior, Volume 34,2014, Pages 301-314, ISSN 0747-5632,https://doi.org/10.1016/j.chb.2013.10.035.

Laurenti, Rafael & Acuña, Fernando. (2019). Exploring antecedents of behavioural intention and preferences in online peer-to-peer resource sharing: A Swedish university setting. Sustainable Production and Consumption. 21. 10.1016/j.spc.2019.10.002.

Chao CM. Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Front Psychol. 2019 Jul 16;10:1652. doi: 10.3389/fpsyg.2019.01652. PMID: 31379679; PMCID: PMC6646805.

Fakhrudin I A, Karyanto P, Ramli M, Behavioral intention and its relationship with gender: a study of green school students in Surakarta, Indonesia, Journal of Physics: Conference Series, Volume 1022, The 1st International Conference on Science, Mathematics, Environment and Education 16 September 2017, Solo Baru, Indonesia. DOI 10.1088/1742-6596/1022/1/012043

Rakshith M D, Predicting Resident Intention Using Machine Learning. Journal of Data Mining and Management (e-ISSN: 2456-9437), Vol.8 No.1 (2023). https://doi.org/10.46610/JoDMM.2023.v08i01.003

Imane Choukri, Hatim Guermah, Hatim Hafiddi et al. Intention process mining using a context-aware Hidden Markov Model, 09 May 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2904289/v1]

Khodabandelou, Ghazaleh & Hug, Charlotte & Deneckère, Rébecca & Salinesi, Camille. (2014). Unsupervised discovery of intentional process models from event logs. 11th Working Conference on Mining Software Repositories, MSR 2014 - Proceedings. 10.1145/2597073.2597101.

Epure, E.V., Hug, C., Deneckere, R., Brinkkemper, S., 2014. What shall i do next?, in: International Conference on Advanced Information Systems Engineering, Springer. pp. 473–487

Yang, S., Ni, W., Dong, X., Chen, S., Farneth, R.A., Sarcevic, A., Marsic, I., Burd, R.S., 2018. Intention mining in medical process: A case study in trauma resuscitation, in: 2018 IEEE International Conference on Healthcare Informatics (ICHI), IEEE. pp. 36–43.

Q. Huang, X. Xia, D. Lo and G. C. Murphy, "Automating Intention Mining," in IEEE Transactions on Software Engineering, vol. 46, no. 10, pp. 1098-1119, 1 Oct. 2020, doi: 10.1109/TSE.2018.2876340.

A. Di Sorbo, S. Panichella, C. A. Visaggio, M. Di Penta, G. Canfora, and H. C. Gall, “Development emails content analyzer: Intention mining in developer discussions (T),” in Proc. 30th IEEE/ACM Int. Conf. Autom. Softw. Eng., 2015, pp. 12–23.

Leelavathy, S., Nithya, M. Public opinion mining using natural language processing technique for improvisation towards smart city. Int J Speech Technol 24, 561–569 (2021). https://doi.org/10.1007/s10772-020-09766-z

Akulick, Samantha & Mahmoud, El Sayed. (2017). Intent Detection through Text Mining and Analysis SOURCE Citation Intent Detection through Text Mining and Analysis.

Raslan, Eman. (2014). Human Intentions Mining Through Natural Language Text Survey. International Journal of Computer Science and Information Security, Vol. 12. 13.