Abstract
Sentiment analysis can extract information from many text sources such as reviews, news, and blogs; then it classifies them based on their polarity.
Moreover, big data is produced via mobile networks and social media.
Applications of sentiment analysis on big data are used as a way of classifying the opinions into diverse sentiment.
Accordingly, performing sentiment analysis on big data can be helpful for a business to take useful commercial insights from text-oriented content.
However, there are very few comprehensive investigations and profound argument in this context.
The goal of this paper is to provide a comprehensive and systematic investigation of the state-of-the-art techniques and highlight the directions for future research.
In this paper, we used systematic literature review method and in the first step, we obtained 15 351 articles; then, based on different filters, 48 related articles were attained.
We have selected 23 articles based on the year of publication, the relevance of the journal, the completeness of the text, the nonrepeatability of the title, and the page number.
Also, we have categorized big data and sentiment analysis into two classifications: centralized and distributed platforms.
Furthermore, the disadvantages and advantages of the investigated techniques are studied and their key issues are emphasized.
Consequently, this study shows that a better analysis of textual big data in terms of sentiment increases efficiency, flexibility, and intelligence.
By providing comparative information and analyzing the current developments in this area, this paper will directly support academics and practicing professionals for better handling of big data in the field of sentiment analysis.
This study sheds some new light on using sentiment analysis and big data for public opinion estimation and prediction.
REFERENCES
1. Dwivedi A, Pant R, KhariM, Pandey S,Mohan L, PandeM. E-governance and big data framework for e-governance and use of sentiment analysis. Available at SSRN 3382731. 2019.
2. Ramanujam RS, Nancyamala R, Nivedha J, Kokila J. Sentiment analysis using big data. Paper presented at: 2015 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC); 2015:0480-0484.
3. Darbandi M, Haghgoo S, Hajiali M, Khabir A. Prediction and estimation of next demands of cloud users based on their comments in CRM and previous usages. Paper presented at: 2018 International Conference on Communication, Computing and Internet of Things (IC3IoT); 2018:81-86.
4. Banic L, Mihanovi ́ c A, Brakus M. Using big data and sentiment analysis in product evaluation. Paper presented at: 2013 36th International Convention ́on Information and Communication Technology, Electronics and Microelectronics (MIPRO); 2013:1149-1154.
5. Jinturkar M, Gotmare P. Sentiment analysis of customer review data using big data: a survey. Int J Comput Appl. 2016;975:8887.
6. Sehgal D, Agarwal AK. Real-time sentiment analysis of big data applications using Twitter data with Hadoop framework. Soft Computing: Theories and Applications. Singapore: Springer; 2018:765-772.
7. Karimkhan M, Bhatia JB. Sentiment Analysis and Big Data Processing. Haryana, India: IJCSC; 2014.
8. Bohlouli M, Dalter J, Dornhöfer M, Zenkert J, Fathi M. Knowledge discovery from social media using big data-provided sentiment analysis (SoMABiT). J Inf Sci. 2015;41:779-798.
9. Kurian D, Vishnupriya S, Ramesh R, et al. Big data sentiment analysis using hadoop. Int J Innov Res Sci Technol. 2015;1:92-96.
10. Pashazadeh A, Navimipour NJ. Big data handling mechanisms in the healthcare applications: a comprehensive and systematic literature review. J Biomed Inform. 2018;82:47-62.
11. Sehgal D, Agarwal AK. Sentiment analysis of big data applications using Twitter Data with the help of HADOOP framework. Paper presented at: 2016 International Conference System Modeling and Advancement in Research Trends (SMART); 2016:251-255.
12. Wang H, Xu Z, Fujita H, Liu S. Towards felicitous decision making: an overview on challenges and trends of big data. Inform Sci. 2016;367:747-765.
13. Chandana R, Harshitha D, Ramachandra A. Big data migration and sentiment analysis of real time events using hadoop ecosystem. Paper presented at: International Conference on Intelligent Data Communication Technologies and Internet of Things; 2018:764-770.
14. Roshanfekr B, Khadivi S, Rahmati M. Sentiment analysis using deep learning on Persian texts. Paper presented at: 2017 Iranian Conference on Electrical Engineering (ICEE); 2017:1503-1508.
15. Han Z, Wu J, Huang C, Huang Q, Zhao M. A review on sentiment discovery and analysis of educational big-data. WIRES Data Min Knowl Discov. 2019;10(1):e1328.
16. Kim K, Lee J. Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction. Pattern Recogn. 2014;47:758-768.
17. Kaur P, Rupal N. A perspective: sentiment analysis and recent trend in opinion mining of Big Data. Int J Adv Computron Manag Studies. 2016;1(3):1-5.
18. Shinde-Pawar M. Formation of smart sentiment analysis technique for Big Data. Int J Innovat Res Comput Commun Eng. 2014;2:7481-7488.
19. Keshavarz H, AbadehMS, AlmasiM. A new lexicon learning algorithm for sentiment analysis of big data. Paper presented at: 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY); 2017:000249-000254.
20. Minanovic A, Gabelica H, Krstic Ž. Big data and sentiment analysis using KNIME: online reviews vs. social media. Paper presented at: 2014 37th ́ International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO); 2014:1464-1468.
21. Tromp E, Pechenizkiy M, Gaber MM. Expressive modeling for trusted big data analytics: techniques and applications in sentiment analysis. Big Data Analytics. 2017;2:5.
22. Pourghebleh B, Navimipour NJ. Data aggregation mechanisms in the Internet of Things: a systematic review of the literature and recommendations for future research. J Netw Comput Appl. 2017;97:23-34.
23. Tsai C-W, Lai C-F, Chao H-C, Vasilakos AV. Big data analytics: a survey. J Big Data. 2015;2:21.
24. Sharef NM, Zin HM, Nadali S. Overview and future opportunities of sentiment analysis approaches for big data. JCS. 2016;12:153-168.
25. Graham G, Meriton RF, Hennelly P. Sentiment Analysis Using KNIME: a Systematic Literature Review of Big Data Logistics. Leeds: The University of Leeds; 2016.
26. Benedetto F, Tedeschi A. Big data sentiment analysis for brand monitoring in social media streams by cloud computing. Sentiment Analysis and Ontology Engineering. Cham: Springer; 2016:341-377.
27. Dandannavar P, Mangalwede S. Sentiment analysis of real world big data—a review of general approaches. Int Sci Press. 2017;10:185-192.
28. Sharma S, Bansal M, Kaushik A. A survey on sentiment analysis for big data. Int J Adv Res Sci Eng. 2017;6:412-416.
29. Balaji SN, Paul PV, Saravanan R. Survey on sentiment analysis based stock prediction using big data analytics. Paper presented at: 2017 Innovations in Power and Advanced Computing Technologies (i-PACT); 2017:1-5.
30. Wamba SF, Akter S, Edwards A, Chopin G, Gnanzou D. How 'big data' can make big impact: findings from a systematic review and a longitudinal case study. Int J Prod Econ. 2015;165:234-246.
31. Qiu J, Wu Q, Ding G, Xu Y, Feng S. A survey of machine learning for big data processing. EURASIP J Adv Sig Process. 2016;2016:67.
32. Russell S, Norvig P. Intelligence Artificielle: Avec plus de 500 Exercices. France: Pearson Education; 2010.
33. Nilsson NJ. Artificial Intelligence: a New Synthesis. Burlington, MA, United States: Morgan Kaufmann; 1998.
34. Cheng OK, Lau R. Big data stream analytics for near real-time sentiment analysis. J Comput Commun. 2015;3:189-195.
35. Fang Y, Chen X, Song Z,Wang T, Cao Y. Modelling propagation of public opinions on microblogging big data using sentiment analysis and compartmental models. Int J Seman Web Inform Syst. 2017;13:11-27.
36. Ragini JR, Anand PR, Bhaskar V. Big data analytics for disaster response and recovery through sentiment analysis. Int J Inf Manag. 2018;42: 13-24.
37. Troisi O, Grimaldi M, Loia F, Maione G. Big data and sentiment analysis to highlight decision behaviours: a case study for student population. Behav Inform Technol. 2018;37:1111-1128.
38. Lau RYK, Zhang W, Xu W. Parallel aspect-oriented sentiment analysis for sales forecasting with big data. Prod Oper Manag. 2018;27: 1775-1794.
39. Chen CIP, Zheng J. Improved big data analytics solution using deep learning model and real-time sentiment data analysis approach. Paper presented at: International Conference on Brain Inspired Cognitive Systems; 2018:579-588.
40. Martínez-Castaño R, Pichel JC, Gamallo P. Polypus: a big data self-deployable architecture for microblogging text extraction and real-time sentiment analysis. arXiv Preprint arXiv:180103710; 2018.
41. Khezr SN, Navimipour NJ. MapReduce and its application in optimization algorithms: a comprehensive study. Majlesi J Multimedia Process. 2015;4:1-5.
42. Dean J, Ghemawat S. MapReduce: a flexible data processing tool. Commun ACM. 2010;53:72-77.
43. Almeer MH. Cloud Hadoop map reduce for remote sensing image analysis. J Emerg Trend Comput Inform Sci. 2012;3:637-644.
44. Nirmal VJ, Amalarethinam DG. Parallel implementation of big data pre-processing algorithms for sentiment analysis of social networking data. Int J Fuzzy Math Arch. 2015;6:149-159.
45. Povoda L, Burget R, Dutta MK. Sentiment analysis based on support vector machine and big data. Paper presented at: 2016 39th International Conference on Telecommunications and Signal Processing (TSP); 2016:543-545.
46. Htet H, Khaing SS, Myint YY. Tweets sentiment analysis for healthcare on big data processing and IoT architecture using maximum entropy classifier. Paper presented at: International Conference on Big Data Analysis and Deep Learning Applications; 2018:28-38.
47. Dwivedi A, Pant R, Pandey S, Pande M, Mittal AK. Benefits, of using big data sentiment analysis and soft computing techniques in E-governance. Int J Recent Technol Eng. 2019;8:3038-3044.
48. Zhang Y, Ren W, Zhu T, Faith E. MoSa: a Modeling and sentiment analysis system for Mobile application big data. Symmetry. 2019;11:115.
49. Rahnama AHA. Distributed real-time sentiment analysis for big data social streams Paper presented at: 2014 International Conference on Control, Decision and Information Technologies (CoDIT); 2014:789-794.
50. Liu B, Blasch E, Chen Y, Shen D, Chen G. Scalable sentiment classification for big data analysis using naive bayes classifier. Paper presented at: 2013 IEEE International Conference on Big Data; 2013:99-104.
