Sentiments Unleashed : Pioneering the Frontier of Sentiment Analysis through Cutting-Edge Applications and Methodologies

Ravi Lourdusamy; Prabakaran Thangavel; Johnbosco S

doi:10.32628/CSEIT24105105

Authors

Ravi Lourdusamy Sacred Heart College (Autonomous), Tirupattur(DT), Thiruvalluvar University, Tamilnadu, India Author
Prabakaran Thangavel Sacred Heart College (Autonomous), Tirupattur(DT), Thiruvalluvar University, Tamilnadu, India Author
Johnbosco S Sacred Heart College (Autonomous), Tirupattur(DT), Thiruvalluvar University, Tamilnadu, India Author

DOI:

https://doi.org/10.32628/CSEIT24105105

Keywords:

Emotions, Attitudes, Opinions, Lexicon-Based, Machine Learning

Abstract

Sentiment analysis is a powerful tool within Natural Language Processing (NLP), assists businesses and researchers in discerning the emotions, attitudes, and beliefs conveyed in written language. The increasing availability of textual data, including social media posts, product reviews, and customer feedback, has driven a significant increase in the demand for sentiment analysis. The primary goal is to uncover underlying emotions expressed through diverse word combinations, sentences, or paragraphs. By scrutinizing language nuances, it identifies a wide range of emotions, from joy and sadness to criticism and admiration. This profound understanding, facilitated by sentiment analysis, provides valuable insights into public perception, market trends, and consumer behaviours, empowering businesses to refine strategies, improve products, and enhance customer experiences. As sentiment analysis tools and techniques continue to advance, they present opportunities for progress, assisting businesses in navigating shifts in public opinion and enabling researchers to delve into the intricacies of human expression through text. With the continuous surge in textual data, sentiment analysis retains its significance in guiding stakeholders from diverse industries toward informed decisions and a comprehensive understanding of human sentiment. This research explores advanced approaches in sentiment analysis, including machine learning, lexicon-based methods, hybrid and ontology-based models, and multi-modal techniques, examining various levels, classifications, and methodologies.

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References

Madhoushi, Zohreh, Abdul Razak Hamdan, and Suhaila Zainudin. "Sentiment analysis techniques in recent works." 2015 science and information conference (SAI). IEEE, 2015. DOI: https://doi.org/10.1109/SAI.2015.7237157

Alessia, D., et al. "Approaches, tools and applications for sentiment analysis implementation." International Journal of Computer Applications 125.3 (2015). DOI: https://doi.org/10.5120/ijca2015905866

Bhonde, Swati B., and Jayashree R. Prasad. "Sentiment Analysis-Methods, Applications & Challenges." International Journal of Electronics Communication and Computer Engineering 6.6 (2015): 634.

Piryani, Rajesh, D. Madhavi, and Vivek Kumar Singh. "Analytical mapping of opinion mining and sentiment analysis research during 2000–2015." Information Processing & Management 53.1 (2017): 122-150. DOI: https://doi.org/10.1016/j.ipm.2016.07.001

Bongirwar, Vrushali K. "A survey on sentence level sentiment analysis." International Journal of Computer Science Trends and Technology (IJCST) 3.3 (2015): 110-113.

Ravi, Kumar, and Vadlamani Ravi. "A survey on opinion mining and sentiment analysis: tasks, approaches and applications." Knowledge-based systems 89 (2015): 14-46. DOI: https://doi.org/10.1016/j.knosys.2015.06.015

Devika, M. Dª, Cª Sunitha, and Amal Ganesh. "Sentiment analysis: a comparative study on different approaches." Procedia Computer Science 87 (2016): 44-49. DOI: https://doi.org/10.1016/j.procs.2016.05.124

Banker, Shreya, and Rupal Patel. "A brief review of sentiment analysis methods." International Journal of Information Sciences and Techniques (IJIST) 6.1/2 (2016). DOI: https://doi.org/10.5121/ijist.2016.6210

Dubey, Gaurav, Ajay Rana, and JayanthiRanjan. "A research study of sentiment analysis and various techniques of sentiment classification." International Journal of Data Analysis Techniques and Strategies 8.2 (2016): 122-142. DOI: https://doi.org/10.1504/IJDATS.2016.077485

Kumar, Praveen, and Umesh Chandra Jaiswal. "A comparative study on sentiment analysis and opinion mining." Int J Eng Technol 8.2 (2016): 938-943.

Sinha, Harsha, and Arashdeep Kaur. "A detailed survey and comparative study of sentiment analysis algorithms." 2016 2nd International Conference on Communication Control and Intelligent Systems (CCIS). IEEE, 2016. DOI: https://doi.org/10.1109/CCIntelS.2016.7878208

Ribeiro, Filipe N., et al. "Sentibench-a benchmark comparison of state-of-the-practice sentiment analysis methods." EPJ Data Science 5 (2016): 1-29. DOI: https://doi.org/10.1140/epjds/s13688-016-0085-1

Astya, Parmanand. "Sentiment analysis: approaches and open issues." 2017 International Conference on computing, Communication and automation (ICCCA). IEEE, 2017.

Verma, Binita, and Ramjeevan Singh Thakur. "Sentiment analysis using lexicon and machine learning-based approaches: A survey." Proceedings of International Conference on Recent Advancement on Computer and Communication: ICRAC 2017. Springer Singapore, 2018. DOI: https://doi.org/10.1007/978-981-10-8198-9_46

Jain, Shubham Kumar, and Pardeep Singh. "Systematic survey on sentiment analysis." 2018 first international conference on secure cyber computing and communication (ICSCCC). IEEE, 2018. DOI: https://doi.org/10.1109/ICSCCC.2018.8703370

Ahmad, Munir, et al. "Sentiment analysis using SVM: a systematic literature review." International Journal of Advanced Computer Science and Applications 9.2 (2018). DOI: https://doi.org/10.14569/IJACSA.2018.090226

Birjali, Marouane, Mohammed Kasri, and AbderrahimBeni-Hssane. "A comprehensive survey on sentiment analysis: Approaches, challenges and trends." Knowledge-Based Systems 226 (2021): 107134.

Chakriswaran, Priya, et al. "Emotion AI-driven sentiment analysis: A survey, future research directions, and open issues." Applied Sciences 9.24 (2019): 5462. DOI: https://doi.org/10.3390/app9245462

Yaakub, MohdRidzwan, Muhammad Iqbal Abu Latiffi, and LiyanaSafraZaabar. "A review on sentiment analysis techniques and applications." IOP conference series: materials science and engineering. Vol. 551. No. 1. IOP Publishing, 2019. DOI: https://doi.org/10.1088/1757-899X/551/1/012070

Aqlan, Ameen Abdullah Qaid, B. Manjula, and R. LakshmanNaik. "A study of sentiment analysis: concepts, techniques, and challenges." Proceedings of International Conference on Computational Intelligence and Data Engineering: Proceedings of ICCIDE 2018. Springer Singapore, 2019. DOI: https://doi.org/10.1007/978-981-13-6459-4_16

Karthikeyan, T., et al. "Personalized content extraction and text classification using effective web scraping techniques." International Journal of Web Portals (IJWP) 11.2 (2019): 41-52. DOI: https://doi.org/10.4018/IJWP.2019070103

Ortis, Alessandro, Giovanni Maria Farinella, and SebastianoBattiato. "An Overview on Image Sentiment Analysis: Methods, Datasets and Current Challenges." ICETE (1) (2019): 296-306 DOI: https://doi.org/10.5220/0007909602900300

Mohsen, Amr Mansour, Amira M. Idrees, and Hesham Ahmed Hassan. "Emotion analysis for opinion mining from text: a comparative study." International Journal of e-Collaboration (IJeC) 15.1 (2019): 38-58. DOI: https://doi.org/10.4018/IJeC.2019010103

Hemmatian, Fatemeh, and Mohammad Karim Sohrabi. "A survey on classification techniques for opinion mining and sentiment analysis." Artificial intelligence review 52.3 (2019): 1495-1545. DOI: https://doi.org/10.1007/s10462-017-9599-6

Nassr, Zineb, NawalSael, and FaouziaBenabbou. "A comparative study of sentiment analysis approaches." Proceedings of the 4th International Conference on Smart City Applications. 2019 DOI: https://doi.org/10.1145/3368756.3369078

Sindhu, Irum, et al. "Aspect-based opinion mining on student’s feedback for faculty teaching performance evaluation." IEEE Access 7 (2019): 108729-108741. DOI: https://doi.org/10.1109/ACCESS.2019.2928872

Hajrizi, Rinor, and Krenare Pireva Nuçi. "Aspect-based sentiment analysis in education domain." arXiv preprint arXiv:2010.01429 (2020).

Rajput, Gaurav Kumar, Ashok Kumar, and Shakti Kundu. "A Comparative Study on Sentiment Analysis Approaches and Methods." 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART). IEEE, 2020. DOI: https://doi.org/10.1109/SMART50582.2020.9337106

Saju, Binju, Siji Jose, and Amal Antony. "Comprehensive Study on Sentiment Analysis: Types, Approaches, Recent Applications, Tools and APIs." 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA). IEEE, 2020. DOI: https://doi.org/10.1109/ACCTHPA49271.2020.9213209

AnvarShathik, J., and K. Krishna Prasad. "A literature review on application of sentiment analysis using machine learning techniques." International Journal of Applied Engineering and Management Letters (IJAEML) 4.2 (2020): 41-77.

Birjali, Marouane, Mohammed Kasri, and AbderrahimBeni-Hssane. "A comprehensive survey on sentiment analysis: Approaches, challenges and trends." Knowledge-Based Systems 226 (2021): 107134. DOI: https://doi.org/10.1016/j.knosys.2021.107134

Alamoodi, Abdullah Hussein, et al. "Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review." Expert systems with applications 167 (2021): 114155. DOI: https://doi.org/10.1016/j.eswa.2020.114155

Zad, Samira, et al. "A survey on concept-level sentiment analysis techniques of textual data." 2021 IEEE World AI IoT Congress (AIIoT). IEEE, 2021. DOI: https://doi.org/10.1109/AIIoT52608.2021.9454169

Tripathi, Vikas, and Dibyahash Bordoloi. "Employing a Similarity-based Clustering approach for effective text summary." Webology 18.5 (2021): 3117-3125. DOI: https://doi.org/10.29121/WEB/V18I5/44

Singh, LoitongbamGyanendro, and SanasamRanbir Singh. "Empirical study of sentiment analysis tools and techniques on societal topics." Journal of Intelligent Information Systems 56 (2021): 379-407. DOI: https://doi.org/10.1007/s10844-020-00616-7

Nair, Aditya, et al. "Comparative Review on Sentiment analysis-based Recommendation system." 2021 6th International Conference for Convergence in Technology (I2CT). IEEE, 2021 DOI: https://doi.org/10.1109/I2CT51068.2021.9418222

Sudhir, Prajval, and Varun Deshakulkarni Suresh. "Comparative study of various approaches, applications and classifiers for sentiment analysis." Global Transitions Proceedings 2.2 (2021): 205-211. DOI: https://doi.org/10.1016/j.gltp.2021.08.004

Pandian, A. Pasumpon. "Performance evaluation and comparison using deep learning techniques in sentiment analysis." Journal of Soft Computing Paradigm 3.2 (2021): 123-134. DOI: https://doi.org/10.36548/jscp.2021.2.006

Dawei, Wang, et al. "A literature review on text classification and sentiment analysis approaches." Computational Science and Technology: 7th ICCST 2020, Pattaya, Thailand, 29–30 August, 2020 (2021): 305-323. DOI: https://doi.org/10.1007/978-981-33-4069-5_26

Subramanian, R. Raja, et al. "A survey on sentiment analysis." 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2021. DOI: https://doi.org/10.1109/Confluence51648.2021.9377136

Thangavel, Prabakaran, and Ravi Lourduswamy. "Sentimental Analysis-A Survey of Some Existing Studies." ISIC. 2021.

Al-Otaibi, Shaha T., and Amal A. Al-Rasheed. "A review and comparative analysis of sentiment analysis techniques." Informatica 46.6 (2022). DOI: https://doi.org/10.31449/inf.v46i6.3991

Zhu, Yi, et al. "Sentiment Analysis Methods: Survey and Evaluation." Available at SSRN 4191581 (2022). DOI: https://doi.org/10.2139/ssrn.4191581

Govindarajan, M. "Approaches and applications for sentiment analysis: a literature review." Data mining approaches for big data and sentiment analysis in social media (2022): 1-23. DOI: https://doi.org/10.4018/978-1-7998-8413-2.ch001

Sharma, Sonali, et al. "A Critical Review on Sentiment Analysis Techniques." 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM). IEEE, 2022. DOI: https://doi.org/10.1109/ICIEM54221.2022.9853140

Cui, Jingfeng, et al. "Survey on sentiment analysis: evolution of research methods and topics." Artificial Intelligence Review (2023): 1-42.

Bordoloi, Monali, and Saroj Kumar Biswas. "Sentiment analysis: A survey on design framework, applications and future scopes." Artificial Intelligence Review (2023): 1-56. DOI: https://doi.org/10.1007/s10462-023-10442-2

Ali, Lafta Raheem, BassamNoori Shaker, and Sabah AbdulazeezJebur. "An extensive study of sentiment analysis techniques: A survey." AIP Conference Proceedings. Vol. 2591. No. 1. AIP Publishing, 2023. DOI: https://doi.org/10.1063/5.0119604

Ullah, Arif, SundasNaqeeb Khan, and NazriMohdNawi. "Review on sentiment analysis for text classification techniques from 2010 to 2021." Multimedia Tools and Applications 82.6 (2023): 8137-8193. DOI: https://doi.org/10.1007/s11042-022-14112-3

Das, Ringki, and ThoudamDoren Singh. "Multimodal sentiment analysis: A survey of methods, trends and challenges." ACM Computing Surveys (2023). DOI: https://doi.org/10.1145/3586075

Thangavel, Prabakaran, and Ravi Lourdusamy. "A lexicon-based approach for sentiment analysis of multimodal content in tweets." Multimedia Tools and Applications (2023): 1-24. DOI: https://doi.org/10.1007/s11042-023-14411-3

Hartmann, Jochen, et al. "More than a feeling: Accuracy and application of sentiment analysis." International Journal of Research in Marketing 40.1 (2023): 75-87. DOI: https://doi.org/10.1016/j.ijresmar.2022.05.005