Harnessing Sentiment Analysis Methodologies for Business Intelligence Enhancement and Governance Intelligence Evaluation
Keywords:
Business Intelligence, Feature Extraction, Governance Intelligence, Recommendation System, Review SummarizationAbstract
The growing popularity of the electronic regime insisted on a larger number of individuals sharing their feelings through different forums.in social media. In today’s business world interaction and correspondence through media have become part and parcel of the exchange of views and opinions between individual and mass communications. Consumers share their experiences with reviews; moviegoers give their comments on films while tourists express their hotel views. One thing that is common among them is to deliberate respective sentiments on the concerned issues. Several thousands of people deliver their sentimental outbursts in a day. The collective sentiment of such a huge volume of review comments needs an effective mechanism to be addressed. Sentiment analysis is the state-of-the-art process that helps in evaluating opinions and expressions. In this paper; the components of sentiment analysis are covered with three subprocesses namely feature extraction, methodologies and evaluation performed in the process of sentiment analysis. We also discuss applications of sentiment analysis in the areas of business intelligence, recommendation systems, governance intelligence and review summarization.
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References
K. Ravi, V. Ravi, “A Survey on Opinion Mining and Sentiment analysis: tasks, approaches and applications”, Knowledge-Based Systems, Vol. 89, pp. 14-46, ScienceDirect, 2015, doi: 10.1016/j.knosys.2015.06.015
H.Kaur, V. Mangat, Nidhi, “A Survey of Sentiment Analysis Techniques”, In: Proc, on International Conf. on I-SMAC (Io T in Social, Mobile, Analysis and Cloud), pp. 921-925, IEEE, 2017, doi:10.1109/I-SMAC.2017.8058315
H.N.T. Xenan, A.C. Le, L.M. Nguyen, “Linguistic Features for Subjectivity Classification”, In: Proc. On International Conf. on Asian Language Processing, IEEE, pp. 17-20, 2012, doi:10.1109/IALP.2012.47
B.A. Rachid, H. Azza, B.G. Henda, “Sentiment Analysis Approaches based on Granularity Levels”, In: Proc. on the 14th International Conf. on Web Information Systems and Technologies, WEBIST, Vol.1, pp. 324-331, SCITEPRESS, 2018, doi:10.5220/0007187603240331
N.S.Joshi, S.A.Liket, “A Survey on Feature Level Sentiment Analysis”, International Journal of Computer Science Information Technologies Vol.5, No.4, pp. 5422-5425, IJCSIT, 2014
R. Sharma, S. Nigam, R. Jain, “Opinion Mining of Movie Reviews at Document Level”, International Journal on Information Theory(IJIT), Vol. 3, No. 3, pp 13-21, IJIT, 2014, doi:10.5121/ijit.2014.3302
N. Farra, E. Challita, R. A. Assi, H. Hajj, “Sentence-Level and Document-Level Sentiment Mining for Arabic Texts”, In: Proc. on International Conf. on Data Mining Workshops, Sydney, NSW, IEEE, pp. 1114-1119, ACM, 2010, doi: 10.1109/ICDMW.2010.95
R.S.Jagdale, V.S.Shivsat, S.Deshmukh, “Sentiment Analysis on Product Reviews Using Machine Learning Techniques”, In: Proc. on AISC, pp. 639-647, Springer, 2017, doi:10.1007/978-981-13-0617-4_61
N.Engonopoulos, A.Lazaridou, G.Paliouras, K.Chandrinos, “A Word-Level Method for Entity-Level Sentiment Analysis”, In: Proc. on the International Conf. on Web Intelligence, Mining and Semantics, ACM, pp.1-9, May 2011, doi:10.1145/1988688.1988703
R. Chen, Y. Zhou, L. Zhang, X. Duan, “Word-level sentiment analysis with reinforcement learning”, In: Proc.on IOP Conf. Series Material Science and Engineering, pp.1-6, IOPSCIENCE, 2019, doi:10.1088/1757-899X/490/6/062063
M. Arora, V. Kausal, “Character Level embedding with Convolutional neural network for text normalization of unstructured data for twitter sentiment analysis”, Int. Jour, of Social Network Analysis&Mining, pp. 1-14, Springer, 2019, doi:10.1007/s13278-019-0557-y
M. S. Haydar, M. Al Hela, S. A. Hossain, “Sentiment Extraction from Bangla Text: A Character Level Supervised Recurrent Neural Network Approach”, In Proc on International Conf. on Computer, Communication, Chemical, Material and Electronic Engineering, pp. 1-4, IEEE, 2018, doi:10.1109/IC4ME2.2018.8465606
B.H. Kasthuriarachchy, K.D.Zoysa, H.L. Premaratne, “Enhanced bag-of-words model for phrase-level sentiment analysis”,In: Proc. on 14th International Conference on Advances in ICT for Emerging Regions, pp. 210-214, Colombo, IEEE, 2014, doi:10.1109/ICTER.2014.7083903
T. Wilson, J. Wiebe, P. Hoffman, “Recognizing Contextual Polarity in Phrase Level Sentiment Analysis”, In: Proceeding of Human Language Technology Conf. and Conf. on Empirical Methods in Natural Processing, pp.347-354, Vancuver, ACM, 2015, doi:10.3115/1220575.1220619
E.Cambria, “An Introduction to Concept-Level Sentiment Analysis”, In: Castro F, Gelbukh A., González M. (Eds) Advances in Soft Computing and Its Applications. MICAI, pp. 478-483, Springer, 2013, doi: 10.1007/978-3-642-45111-9_41
K. Akiyama, K. Mitsuzamai, N. Kazuya, T. Kumarioto, A. Nadamoto, “Clause Level Negative Opinion Analysis for Classifying Reviews on multiple domain”, In: Proc. of 20th International Conf. on Information Integration and Web-based Applications & Services, pp. 112–13, ACM, 2018, doi:10.1145/3282373.3282405
F. Koto, M. Adriani, “The Use of POS Sequence for Analysing Sentence Pattern in Twitter Sentiment Analysis”, In: Proc. on 29th International Conf. on Advanced Information Networking Application Workshops, pp.547-551, IEEE, 2015, doi:10.1109/WAINA.2015.58
T. Nakagawa, T. Kawada, K. Inui, S. Kurohashi, “Extracting Subjective and Objective Evaluative from the Web”. Second International Symposium on Universal Communication, pp.251-258, ACM, 2008, doi:10.1109/ISUC.2008.17
Y. Liu, A. Li, L.Duan, H.Wang, “Characterised by Subjective Clues on Subjective Text Recognition”, In: Proc. on International Conf. on Cloud Computing and Big data, pp. 20-27, IEEE, 2014, doi:10.1109/CCBD.2014.19
H. Keshavarz, M.S. Abadeh, “Sublex: Generating Subjectivity Lexicon Using Genetic Algorithm for Subjectivity Classification of Big Social Data”, In: Proc. on 1st Conference on Swarm Intelligence and Evolutionary Computation, pp. 136-141, IEEE, 2016, doi:10.1109/CSIEC.2016.7482126
W. Medhat, A. Hassan, H. Korashy, “Sentiment Analysis Algorithm and Application: A Survey”, Ain Shams Engineering Journal, Vol. 5, No. 4, pp. 1093-1111, ScienceDirect, 2014, doi: 10.1016/j.asej.2014.04.011
N.A. Abdulla, N. A. Ahmed, M. A. Shehab, M. Al-Ayyoub, “Arabic sentiment analysis: Lexicon-based and corpus-based”, In: Proc. of Jordan Conference on Applied Electrical Engineering and Computing Technologies, pp.1-6, IEEE, 2013, doi:10.1109/AEECT.2013.6716448
P.D.T. Chathuranga, S.A.S. Lorensuhewa, M.A.L. Kalyani, “Sinhala Sentiment Analysis using Corpus based Sentiment Lexicon”, In: Proc. of 19th International Conference on Advances in ICT for Emerging Regions, pp. 1-7, IEEE, 2019, doi:10.1109/ICTer48817.2019.9023671
V. Bonta, N. Kumaresh, N. Janardhan, “A Comprehensive Study on Lexicon Based Approaches for Sentiment Analysis”, In: Asian Journal of Computer Science and Technology, Vol.8 No.52, pp.1-6, AJCST, 2019, doi:10.51983/ajcst-2019.8.S2.2037
L. Cruz, J. Ochoa, M. Roche, P. Poncelet, “Dictionary-Based Sentiment Analysis Applied to a Specific Domain”, In Proc. on Communication in Computer and Information Science, Vol. 656, pp. 57-68, Springer, 2017, doi:10.1007/978-3-319-55209-5_5
M. Raut, M. Kulkarni, S. Barve, “A Survey of Approaches for sentiment Analysis and Application of OMSA beyond Product Evaluation”, International Journal of Engineering Trends and Technology Vol. 46, No. 7, pp. 396-400, IJETT,2017.
R. Alfrjani, T. Osman, G. Cosma, “A Hybrid Semantic Knowledge base – Machine Learning Approach for Opinion Mining”, Data & Knowledge Engineering, Vol. 121, pp. 88-108, ScienceDirect, 2019, doi: 10.1016/j.datak.2019.05.002
Z. Singla, S. Randhawa, S. Jain,“Sentiment analysis of customer product reviews using machine learning”, In: Proc. of International Conf. on Intelligent Computing and Control, pp. 1-5, IEEE, 2017, doi:10.1109/I2C2.2017.8321910
N.M. Shelke, S. Deshpande, V. Thakre, “Exploiting Expectation Maximization Algorithm for Sentiment Analysis of Product Reviews”, In: Proc. of International Conf. on Inventive Communication and Computational Technologies, pp. 390-396, IEEE, 2017, doi:10.1109/ICICCT.2017.7975226
S. Thara, S. Sidharth, “Aspect based Sentiment Classification: SVD features”,In Proc. of International Conf. on Advances in Computing, Communication and Informatics, pp. 2370-2374, IEEE, 2017, doi:10.1109/ICACCI.2017.8126201
M. Yuan, Y. Ouyang, Z. Xiong , H. Sheng, “Sentiment Classification of Web Review using Association Rules”, In: Proc. of Int Conf. on Online Communities and Social Computers, pp 442-450, Springer, 2013, doi:10.1007/978-3-642-39371-6_49
H. Rehioui, A. Idrissi, “New Clustering Algorithms for Twitter Sentiment Analysis”, In: IEEE Systems Journal, Vol. 14, No. 1, pp. 530-537, IEEE, 2019, doi:10.1109/JSYST.2019.2912759
Z. Hu, J. Hu, W. Ding , X. Zheng, “Review Sentiment Analysis Based on Deep Learning”, In: Proc. of 12th International Conference on e-Business Engineering, pp. 87-94, ACM, 2015, doi:10.1109/ICEBE.2015.24
Y. Chen, B. Zhou, W. Zhang, W. Gong, G. Sun, “Sentiment Analysis Based on Deep Learning and its Application in Screening for Perinatal Depression”, In Proc. of Third Int. Conf. on Data Science in Cyberspace, pp. 451- 456, IEEE, 2018, doi:10.1109/DSC.2018.00073
J. Wang, C. Sun, S. Li, J. Wang, , “Human like Decision Making: Document-level Aspect Sentiment classification via Hierarchical Reinforcement learning”, In Proc on 9th Int. Jt Conf. on Natural Language Processing, pp. 5581-5590, 2019
P.F.Kurnia, Suharjito, ”Business Intelligence Model to Analyze Social Media Information”, Procedia Computer Science,Vol. 135, pp 5-14,2018, ISSN 1877-0509, doi:/10.1016/j.procs.2018.08.144
A.Nasser, & H.Sever, “A concept-based sentiment analysis approach for Arabic”, Int. Arab J. Inf. Technol., 17, 778-788.
Hung BT (2020), “Integrating sentiment analysis in recommender systems”, Reliability and statistical computing. Springer, Cham, pp 127–137
R. Pradhan, V. Khandelwal, A. Chaturvedi and D. K. Sharma, "Recommendation System using Lexicon Based Sentimental Analysis with collaborative filtering," 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), 2020, pp. 129-132, doi: 10.1109/PARC49193.2020.236571.
Alqaryouti, Omar, Nur Siyam, Azza R Abdel Monem and Khaled F. Shaalan. “Aspect-based sentiment analysis using smart government review data” Applied Computing and Informatics”, 2020
Corallo, A., Fortunato, L., Matera, M., Alessi, M., Camillò, A., Chetta, V. Storelli, D. (2015, July), “Sentiment analysis for government: An optimized approach”, In International Workshop on Machine Learning and Data Mining in Pattern Recognition (pp. 98–112). Cham: Spring
Yang M, Qu Q, Shen Y, Liu Q, Zhao W, Zhu J (2018) Aspect and sentiment aware abstractive review summarization. In: Proceedings of the 27th international conference on computational linguistics, pp 1110–1120
F. Alsaqer and S. Sasi, "Movie review summarization and sentiment analysis using rapidminer," 2017 International Conference on Networks & Advances in Computational Technologies (NetACT), 2017, pp. 329-335, doi: 10.1109/NETACT.2017.8076790
Subburayalu, G., Duraivelu, H., Raveendran, A. P., Arunachalam, R., Kongara, D., & Thangavel, C. (2021). Cluster based malicious node detection system for mobile ad-hoc network using ANFIS classifier. Journal of Applied Security Research, 1–19. https://doi.org/10.1080/19361610.2021.2002118
Gopalakrishnan, S., & Kumar, P. M. (2016). Performance analysis of malicious node detection and elimination using clustering approach on MANET. Circuits and Systems, 07(6), 748–758. https://doi.org/10.4236/cs.2016.76064
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