Features level sentiment mining in enterprise systems from informal text corpus using machine learning techniques

References

• Alakbarov, R. 2022. “An Optimization Model for Task Scheduling in Mobile Cloud Computing.” International Journal of Cloud Applications and Computing (IJCAC) 12 (1): 700–725. https://doi.org/10.4018/IJCAC.297102.
   Google Scholar
• Al-Smadi, M., O. Qawasmeh, M. Al-Ayyoub, Y. Jararweh, and B. Gupta. 2018. “Deep Recurrent Neural Network Vs. Support Vector Machine for Aspect-Based Sentiment Analysis of Arabic hotels’ Reviews.” Journal of Computational Science 27:386–393. https://doi.org/10.1016/j.jocs.2017.11.006.
   Web of Science ®Google Scholar
• Arora, P., A. Bakliwal, and V. Varma. 2012. “Hindi Subjective Lexicon Generation Using WordNet Graph Traversal.” International Journal of Computational Linguistics and Applications 43 (10): 25–28. https://doi.org/10.5120/6141-8386.
   Google Scholar
• Asgari, T., A. Daneshvar, A. P. Chobar, M. Ebrahimi, and S. Abrahamyan. 2022. “Identifying Key Success Factors for Startups with Sentiment Analysis Using Text Data Mining.” International Journal of Engineering Business Management 14:18479790221131612. https://doi.org/10.1177/18479790221131612.
   Web of Science ®Google Scholar
• Bakliwal, A., P. Arora, and V. Varma 2012, May. Hindi Subjective Lexicon: A Lexical Resource for Hindi Polarity Classification. In Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC), Istanbul, Turkey (pp. 1189–1196).
   Google Scholar
• Barbosa, A., I. I. Bittencourt, S. W. Siqueira, D. Dermeval, and N. J. Cruz. 2022. “A context-independent ontological linked data alignment approach to instance matching.” International Journal on Semantic Web and Information Systems (IJSWIS) 18 (1): 1–29. https://doi.org/10.4018/IJSWIS.295977.
   Web of Science ®Google Scholar
• Barley, W. C., J. W. Treem, and T. Kuhn. 2018. “Valuing Multiple Trajectories of Knowledge: A Critical Review and Agenda for Knowledge Management Research.” Academy of Management Annals 12 (1): 278–317. https://doi.org/10.5465/annals.2016.0041.
   Web of Science ®Google Scholar
• Basiri, M. E., A. Kabiri, M. Abdar, W. K. Mashwani, N. Y. Yen, and J. C. Hung. 2020. “The Effect of Aggregation Methods on Sentiment Classification in Persian Reviews.” Enterprise Information Systems 14 (9–10): 1394–1421. https://doi.org/10.1080/17517575.2019.1669829.
   Google Scholar
• Bele, N., P. K. Panigrahi, and S. K. Srivastava. 2017. “Political Sentiment Mining: A New Age Intelligence Tool for Business Strategy Formulation.” International Journal of Business Intelligence Research (IJBIR) 8 (1): 55–70. https://doi.org/10.4018/IJBIR.2017010104.
   Google Scholar
• Benthaus, J., M. Risius, and R. Beck. 2016. “Social Media Management Strategies for Organizational Impression Management and Their Effect on Public Perception.” The Journal of Strategic Information Systems 25 (2): 127–139. https://doi.org/10.1016/j.jsis.2015.12.001.
   Web of Science ®Google Scholar
• Bhardwaj, A., and K. Kaushik. 2022. “Predictive Analytics-Based Cybersecurity Framework for Cloud Infrastructure.” International Journal of Cloud Applications and Computing (IJCAC) 12 (1): 1–20. https://doi.org/10.4018/IJCAC.297106.
   Google Scholar
• Calzolari, N., C. J. Fillmore, R. Grishman, N. Ide, A. Lenci, C. MacLeod, and A. Zampolli. 2002. Towards Best Practice for Multiword Expressions in Computational Lexicons. In LREC, 1934–1940. Vol. 2.
   Google Scholar
• Casillo, M., B. B. Gupta, M. Lombardi, A. Lorusso, D. Santaniello, and C. Valentino. 2022. “Context Aware Recommender Systems: A Novel Approach Based on Matrix Factorization and Contextual Bias.” Electronics 11 (7): 1003. https://doi.org/10.3390/electronics11071003.
   Web of Science ®Google Scholar
• Chen, X., J. Li, and Y. F. Zhang. 2022. “Multidirectional Gradient Feature with Shape Index for Effective Texture Classification.” International Journal on Semantic Web and Information Systems (IJSWIS) 18 (1): 1–19. https://doi.org/10.4018/IJSWIS.312183.
   Web of Science ®Google Scholar
• Chen, H., Y. P. Tsang, and C. H. Wu. 2023. “When Text Mining Meets Science Mapping in the Bibliometric Analysis: A Review and Future Opportunities.” International Journal of Engineering Business Management 15:18479790231222349. https://doi.org/10.1177/18479790231222349.
   Web of Science ®Google Scholar
• Chun, H., B. H. Leem, and H. Suh. 2021. “Using Text Analytics to Measure an Effect of Topics and Sentiments on Social-Media Engagement: Focusing on Facebook Fan Page of Toyota.” International Journal of Engineering Business Management 13:18479790211016268. https://doi.org/10.1177/18479790211016268.
   Web of Science ®Google Scholar
• Cui, R., S. Gallino, A. Moreno, D. J. Zhang, R. Ganeshan, and N. R. Sanders. 2018. “The Operational Value of Social Media Information.” Production and Operations Management 27 (10): 1749–1769. https://doi.org/10.1111/poms.12707.
   Web of Science ®Google Scholar
• Dalkir, K. 2017. Knowledge Management in Theory and Practice. London: MIT press.
   Google Scholar
• Dayan, R., P. Heisig, and F. Matos. 2017. “Knowledge Management As a Factor for the Formulation and Implementation of Organization Strategy.” Journal of Knowledge Management 21 (2): 308–329. https://doi.org/10.1108/JKM-02-2016-0068.
   Web of Science ®Google Scholar
• Diwanji, S. 2020. Number of Internet Users in India from 2015 to 2018 with a Forecast Until 2023. Statista. https://www.statista.com/statistics/255146/number-of-internet-users-in-india/
   Google Scholar
• Felix, R., P. A. Rauschnabel, and C. Hinsch. 2017. “Elements of Strategic Social Media Marketing: A Holistic Framework.” Journal of Business Research 70:118–126. https://doi.org/10.1016/j.jbusres.2016.05.001.
   Web of Science ®Google Scholar
• Frawley, W. J., G. Piatetsky-Shapiro, and C. J. Matheus. 1992. “Knowledge Discovery in Databases: An Overview.” AI Magazine 13 (3): 57–57.
   Web of Science ®Google Scholar
• Gaurav, A., B. B. Gupta, C. H. Hsu, A. Castiglione, and K. T. Chui (2021). Machine Learning Technique for Fake News Detection Using Text-Based Word Vector Representation. In Computational Data and Social Networks: 10th International Conference, CSoNet 2021, Virtual Event, Proceedings, November 15–17, 2021, 10 (pp. 340–348). Montreal, Quebec, Canada: Springer International Publishing.
   Google Scholar
• Georgiadou, E., S. Angelopoulos, and H. Drake. 2020. “Big Data Analytics and International Negotiations: Sentiment Analysis of Brexit Negotiating Outcomes.” International Journal of Information Management 51:102048. https://doi.org/10.1016/j.ijinfomgt.2019.102048.
   Web of Science ®Google Scholar
• Grljević, O., Z. Bošnjak, and A. Kovačević. 2022. “Opinion Mining in Higher Education: A Corpus-Based Approach.” Enterprise Information Systems 16 (5): 1773542. https://doi.org/10.1080/17517575.2020.1773542.
   Web of Science ®Google Scholar
• Grobelnik, M., and D. Mladenic. 2005. “Automated Knowledge Discovery in Advanced Knowledge Management.” Journal of Knowledge Management 9 (5): 132–149. https://doi.org/10.1108/13673270510622500.
   Google Scholar
• Gupta, S., and B. B. Gupta. 2018. “POND: Polishing the Execution of Nested Context-Familiar Runtime Dynamic Parsing and Sanitisation of XSS Worms on Online Edge Servers of Fog Computing.” International Journal of Innovative Computing and Applications 9 (2): 116–129. https://doi.org/10.1504/IJICA.2018.092588.
   Google Scholar
• Gu, J., N. D. Vo, and J. J. Jung. 2022. “Contextual Word2Vec model for understanding Chinese out of vocabularies on online social media.” International Journal on Semantic Web and Information Systems (IJSWIS) 18 (1): 1–14. https://doi.org/10.4018/IJSWIS.309428.
   Web of Science ®Google Scholar
• Haselmayer, M., and M. Jenny. 2017. “Sentiment Analysis of Political Communication: Combining a Dictionary Approach with Crowdcoding.” Quality & Quantity 51 (6): 2623–2646. https://doi.org/10.1007/s11135-016-0412-4.
   PubMedGoogle Scholar
• Hislop, D., R. Bosua, and R. Helms. 2018. Knowledge Management in Organizations: A Critical Introduction. United Kingdom: Oxford university press.
   Google Scholar
• Ibrahim, N. F., and X. Wang. 2019. “A Text Analytics Approach for Online Retailing Service Improvement: Evidence from Twitter.” Decision Support Systems 121:37–50. https://doi.org/10.1016/j.dss.2019.03.002.
   Web of Science ®Google Scholar
• Islam, M., F. E. M. Jubayer, and S. I. Ahmed (2017). A Comparative Study on Different Types of Approaches to Bengali Document Categorization. In Proceedings of the International Conference on Engineering Research, Innovation and Education 2017 ICERIE 2017, 13 ̶ 15 January, SUST, Sylhet, Bangladesh.
   Google Scholar
• Ismail, S., T. E. Shishtawy, and A. K. Alsammak. 2022. “A New Alignment Word-Space Approach for Measuring Semantic Similarity for Arabic Text.” International Journal on Semantic Web and Information Systems (IJSWIS) 18 (1): 1–18. https://doi.org/10.4018/IJSWIS.297036.
   Web of Science ®Google Scholar
• Jain, B. (2018, June 27). Hindi Mother Tongue of 44% in India, Bangla Second Most Spoken. Times of India, Retrieved from https://timesofindia.indiatimes.com/india/hindi-mother-tongue-of-44-in-india-bangla-second-most-spoken/articleshow/64755458.cms
   Google Scholar
• Jennex, M. E. 2017. “Big Data, the Internet of Things, and the Revised Knowledge Pyramid.” ACM SIGMIS Database: The DATABASE for Advances in Information Systems 48 (4): 69–79. https://doi.org/10.1145/3158421.3158427.
   Google Scholar
• Jennex, M. E., and S. E. Bartczak. 2013. “A Revised Knowledge Pyramid.” International Journal of Knowledge Management (IJKM) 9 (3): 19–30. https://doi.org/10.4018/ijkm.2013070102.
   Google Scholar
• Jha, V., R. Savitha, P. D. Shenoy, K. R. Venugopal, and A. K. Sangaiah. 2018. “A Novel Sentiment Aware Dictionary for Multi-Domain Sentiment Classification.” Computers & Electrical Engineering 69:585–597. https://doi.org/10.1016/j.compeleceng.2017.10.015.
   Web of Science ®Google Scholar
• Jiao, R., C. Li, G. Xun, T. Zhang, B. B. Gupta, and G. Yan. 2023. A Context-Aware Multi-Event Identification Method for Non-Intrusive Load Monitoring. United States: IEEE Transactions on Consumer Electronics.
   Google Scholar
• Joshi, A., A. R. Balamurali, and P. Bhattacharyya (2010). A Fall-Back Strategy for Sentiment Analysis in Hindi: A Case Study. Proceedings of the 8th ICON, India.
   Google Scholar
• Kane, G. C. 2017. “The Evolutionary Implications of Social Media for Organizational Knowledge Management.” Information and Organization 27 (1): 37–46. https://doi.org/10.1016/j.infoandorg.2017.01.001.
   Web of Science ®Google Scholar
• Kanikar, P., R. Koppisetty, S. Govindan, S. Bhatand, and M. Virani. 2016. “Semantic Analysis on Twitter Data Generated by Indian users’.” International Journal of Scientific & Engineering Research 7 (9): 808–812.
   Google Scholar
• Karami, A., L. S. Bennett, and X. He. 2018. “Mining Public Opinion About Economic Issues: Twitter and the Us Presidential Election.” International Journal of Strategic Decision Sciences (IJSDS) 9 (1): 18–28. https://doi.org/10.4018/IJSDS.2018010102.
   Google Scholar
• Kaur, H., V. Mangat, and N. Krail. 2017. “Dictionary Based Sentiment Analysis of Hinglish Text.” International Journal of Advanced Research in Computer Science 8 (5): 816–822.
   Google Scholar
• Koul, O. N. 2008. “Modern Hindi Grammar.” In Journal of Asian Studies, Vol. 19. Springfield, USA: Dunwoody Press. https://doi.org/10.2307/2943477.
   Google Scholar
• Kulkarni, K. K., A. D. Kalro, D. Sharma, and P. Sharma. 2020. “A Typology of Viral Ad Sharers Using Sentiment Analysis.” Journal of Retailing and Consumer Services 53:101739. https://doi.org/10.1016/j.jretconser.2019.01.008.
   Web of Science ®Google Scholar
• Kumar, S., P. Behera, and G. N. Jha. 2017. “A Classification-Based Approach to the Identification of Multiword Expressions (MWEs) in Magahi Applying SVM.” Procedia computer science 112:594–603. https://doi.org/10.1016/j.procs.2017.08.059.
   Google Scholar
• Kumar, K. A., N. Rajasimha, M. Reddy, A. Rajanarayana, and K. Nadgir. 2015. “Analysis of users’ Sentiments from Kannada Web Documents.” Procedia Computer Science 54:247–256. https://doi.org/10.1016/j.procs.2015.06.029.
   Google Scholar
• Li, C., Q. Liu, and L. Huang. 2021. “Credit Risk Management of Scientific and Technological Enterprises Based on Text Mining.” Enterprise Information Systems 15 (6): 851–867. https://doi.org/10.1080/17517575.2020.1802514.
   Web of Science ®Google Scholar
• Li, X., J. Zhang, Y. Du, J. Zhu, Y. Fan, and X. Chen. 2023. “A Novel Deep Learning-Based Sentiment Analysis Method Enhanced with Emojis in Microblog Social Networks.” Enterprise Information Systems 17 (5): 2037160. https://doi.org/10.1080/17517575.2022.2037160.
   Web of Science ®Google Scholar
• Ma, B., E. Lai, W. Q. Yan, and J. Wu. 2023. “A Privacy-Preserving Word Embedding Text Classification Model Based on Privacy Boundary Constructed by Deep Belief Network.” Multimedia Tools and Applications 83 (10): 1–26. https://doi.org/10.1007/s11042-023-15623-3.
   Web of Science ®Google Scholar
• Mandal, A. K., and R. Sen. 2014. “Supervised Learning Methods for Bangla Web Document Categorization.” International Journal of Artificial Intelligence & Applications (IJAIA) 5 (5): 93–105. https://doi.org/10.5121/ijaia.2014.5508.
   Google Scholar
• Miri, M., M. B. Dowlatshahi, A. Hashemi, M. K. Rafsanjani, B. B. Gupta, and W. Alhalabi. 2022. “Ensemble Feature Selection for Multi‐Label Text Classification: An Intelligent Order Statistics Approach.” International Journal of Intelligent Systems 37 (12): 11319–11341. https://doi.org/10.1002/int.23044.
   Web of Science ®Google Scholar
• Mittal, N., B. Agarwal, G. Chouhan, N. Bania, and P. Pareek (2013, October). Sentiment Analysis of Hindi Reviews Based on Negation and Discourse Relation. In Proceedings of the 11th Workshop on Asian Language Resources, Nagoya, Japan, 45–50.
   Google Scholar
• Mukku, S. S., N. Choudhary, and R. Mamidi (2016). Enhanced Sentiment Classification of Telugu Text using ML Techniques. In Proceedings of the 4th Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2016), IJCAI 2016, 29-34, New York City, USA, July 10, 2016. pp. 29–34
   Google Scholar
• Nongmeikapam, K., D. Khangembam, W. Hemkumar, S. Khuraijam, and S. Bandyopadhyay. 2014. “Verb based manipuri sentiment analysis.” Int J Nat Lang Comput 3 (3): 113–119. https://doi.org/10.5121/ijnlc.2014.3311.
   Google Scholar
• Paul, D., F. Li, M. K. Teja, X. Yu, and R. Frost (2017, August). Compass: Spatio Temporal Sentiment Analysis of US Election What Twitter Says! In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, Halifax, NS, Canada, 1585–1594.
   Google Scholar
• Ragini, J. R., P. R. Anand, and V. Bhaskar. 2018. “Big Data Analytics for Disaster Response and Recovery Through Sentiment Analysis.” International Journal of Information Management 42:13–24. https://doi.org/10.1016/j.ijinfomgt.2018.05.004.
   Web of Science ®Google Scholar
• Rajapathirana, R. J., and Y. Hui. 2018. “Relationship Between Innovation Capability, Innovation Type, and Firm Performance.” Journal of Innovation & Knowledge 3 (1): 44–55. https://doi.org/10.1016/j.jik.2017.06.002.
   Web of Science ®Google Scholar
• Ravi, K., and V. Ravi (2016, March). Sentiment Classification of Hinglish Text. In 2016 3rd International Conference on Recent Advances in Information Technology (RAIT), Dhanbad, India, 641–645. IEEE.
   Google Scholar
• Ravishankar, N., and R. Shriram. 2018. “Grammar Rule-Based Sentiment Categorisation Model for Classification of Tamil Tweets.” International Journal of Intelligent Systems Technologies and Applications 17 (1/2): 89–97. https://doi.org/10.1504/IJISTA.2018.091589.
   Google Scholar
• Salehin, S. S., R. Miah, and M. S. Islam (2020, January). A Comparative Sentiment Analysis on Bengali Facebook Posts. In Proceedings of the International Conference on Computing Advancements, Dhaka, Bangladesh, 1–8.
   Google Scholar
• Sazzed, S., and S. Jayarathna (2019, July). A Sentiment Classification in Bengali and Machine Translated English Corpus. In 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), Los Angeles, CA, USA, 107–114. IEEE.
   Google Scholar
• Sharma, R., S. Nigam, and R. Jain. 2014. “Polarity Detection Movie Reviews in Hindi Language.” International Journal on Computational Sciences & Applications (IJCSA) 4 (4): 49–57. https://doi.org/10.5121/ijcsa.2014.4405.
   Google Scholar
• Singh, R. L., K. Ghosh, K. Nongmeikapam, and S. Bandyopadhyay. 2014. “A Decision Tree Based Word Sense Disambiguation System in Manipuri Language.” Advanced Computing 5 (4): 17–22. https://doi.org/10.5121/acij.2014.5403.
   Google Scholar
• Singh, S. K., and M. K. Sachan. 2021. “Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis.” International Journal on Semantic Web and Information Systems (IJSWIS) 17 (2): 59–78. https://doi.org/10.4018/IJSWIS.2021040104.
   Web of Science ®Google Scholar
• Tayal, D. K., and S. K. Yadav. 2017. “Sentiment Analysis on Social Campaign Swachh Bharat Abhiyan Using Unigram Method.” AI & SOCIETY 32 (4): 633–645. https://doi.org/10.1007/s00146-016-0672-5.
   Google Scholar
• Usai, A., M. Pironti, M. Mital, and C. A. Mejri. 2018. “Knowledge Discovery Out of Text Data: A Systematic Review via Text Mining.” Journal of Knowledge Management 22 (7): 1471–1488. https://doi.org/10.1108/JKM-11-2017-0517.
   Web of Science ®Google Scholar
• Vinodhini, G., and R. M. Chandrasekaran. 2017. “A Sampling Based Sentiment Mining Approach for E-Commerce Applications.” Information Processing & Management 53 (1): 223–236. https://doi.org/10.1016/j.ipm.2016.08.003.
   Web of Science ®Google Scholar
• Vuleta, B. 2020. How Much Data Is Created Every Day? [27 Powerful Stats]. SeedScientific. https://seedscientific.com/how-much-data-is-created-every-day/.
   Google Scholar
• Whitney, J., M. Jennex, A. Elkins, and E. Frost (2018, January). Don’t Want to Get Caught? don’t Say It: The Use of Emojis in Online Human Sex Trafficking Ads. In Proceedings of the 51st Hawaii International Conference on System Sciences, Hawaii.
   Google Scholar
• Wu, J., S. Guo, H. Huang, W. Liu, and Y. Xiang. 2018. “Information and Communications Technologies for Sustainable Development Goals: State-Of-The-Art, Needs and Perspectives.” IEEE Communications Surveys & Tutorials 20 (3): 2389–2406. https://doi.org/10.1109/COMST.2018.2812301.
   Web of Science ®Google Scholar
• Wu, J., S. Guo, J. Li, and D. Zeng. 2016a. “Big Data Meet Green Challenges: Big Data Toward Green Applications.” IEEE Systems Journal 10 (3): 888–900. https://doi.org/10.1109/JSYST.2016.2550530.
   Web of Science ®Google Scholar
• Wu, J., S. Guo, J. Li, and D. Zeng. 2016b. “Big data meet green challenges: Greening big data.” IEEE Systems Journal 10 (3): 873–887. https://doi.org/10.1109/JSYST.2016.2550538.
   Web of Science ®Google Scholar
• Yen, S., M. Moh, and T. S. Moh. 2021. “Detecting Compromised Social Network Accounts Using Deep Learning for Behavior and Text Analyses.” International Journal of Cloud Applications and Computing (IJCAC) 11 (2): 97–109. https://doi.org/10.4018/IJCAC.2021040106.
   Web of Science ®Google Scholar