Thursday, January 17, 2019

Systematic literature review of sentiment analysis on Twitter using soft computing techniques

 



ABSTRACT

Sentiment detection and classification is the latest fad for social analytics on Web. 

With the array of practical applications in healthcare, finance, media, consumer markets, and government, distilling the voice of public to gain insight to target information and reviews is non-trivial. 

With a marked increase in the size, subjectivity, and diversity of social web-data, the vagueness, uncertainty and imprecision within the information has increased manifold. 

Soft computing techniques have been used to handle this fuzziness in practical applications. 

This work is a study to understand the feasibility, scope and relevance of this alliance of using Soft computing techniques for sentiment analysis on Twitter. 

We present a systematic literature review to collate, explore, understand and analyze the efforts and trends in a well-structured manner to identify research gaps defining the future prospects of this coupling. 

The contribution of this paper is significant because firstly the primary focus is to study and evaluate the use of soft computing techniques for sentiment analysis on Twitter and secondly as compared to the previous reviews we adopt a systematic approach to identify, gather empirical evidence, interpret results, critically analyze, and integrate the findings of all relevant high-quality studies to address specific research questions pertaining to the defined research domain.


REFERENCES

1. International Data Corporation. 2017. https://www.idc.com/about/about.jsp. Accessed April 22, 2017.


2. Aggarwal CC, Zhai C. A survey of text classification algorithms. In: Mining Text Data. Boston, MA: Springer Science+Business Media; 2012.


3. Rajman M, Besançon R. Text mining-knowledge extraction from unstructured textual data. Advances in Data Science and Classification: Proceedings of

the 6th Conference of the International Federation of Classification Societies (IFCS-98) Università ‘‘La Sapienza’’, Rome, 21-24 July, 1998. Berlin, Germany:

Springer-Verlag Berlin Heidelberg; 1998:473-480.


4. Kumar A, Sebastian TM. Sentiment analysis on Twitter. Int J Comput Sci Issues. 2012;9(4):372-378.


5. Pang B, Lee L. Opinion mining and sentiment analysis. Found Trends® Inf Retr. 2008;2(1-2):1-135.


6. Kumar A, Sebastian TM. Sentiment analysis: a perspective on its past, present and future. Int J Intell Syst Appl. 2012;4(10):1-14.


7. Reilly T. Web 2.0 Compact Definition: Trying Again. Sebastopol, CA: O'Reilly Media; 2017. http://radar.oreilly.com/2006/12/web-20-compact-

definition-tryi.html. Accessed April 24, 2017.


8. Alfouzan HI. Introduction to SMAC-social mobile analytics and cloud. Int J Sci Eng Res. 2015;6:128-130.


9. Li D, Luo Z, Ding Y, et al. User-level microblogging recommendation incorporating social influence. J Assoc Inf Sci Technol. 2017;68(3):553-568.


10. Pak A, Paroubek P. Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the Seventh Conference on International

Language Resources and Evaluation; 2010; Valletta, Malta.


11. Liu B. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge, UK: Cambridge University Press; 2015.


12. Kumar A, Joshi A. Ontology driven sentiment analysis on social web for government intelligence. In: Proceedings of the Special Collection on

eGovernment Innovations in India; 2017; New Delhi, India.


13. Dave K, Lawrence S, Pennock DM. Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of

the 12th international conference on World Wide Web; 2003; Budapest, Hungary.


14. Kumar A, Khorwal R, Chaudhary S. A survey on sentiment analysis using swarm intelligence. Indian J Sci Technol. 2016;9(39):1-7.


15. Aggarwal CC. Neural Networks and Deep Learning: A Textbook. Cham, Switzerland: Springer International Publishing; 2018.


16. Sivanandam SN, Deepa SN. Principles of Soft Computing (With CD). Hoboken, NJ: Wiley. 2007.


17. Finn S, Mustafaraj E. Learning to discover political activism in the Twitterverse. KI-Künstliche Intelligenz. 2013;27(1):17-24.


18. Saif H, He Y, Fernandez M, Alani H. Contextual semantics for sentiment analysis of Twitter. Inf Process Manag. 

2015;52(1):5-19. https://doi.org/10.

1016/j.ipm.2015.01.005


19. Wu B, Shen H. Analyzing and predicting news popularity on Twitter. Int J Inf Manag. 2015;35(6):702-711.


20. Balas VE, Fodor J, Várkonyi-Kóczy AR, eds. New Concepts and Applications in Soft Computing. Berlin, Germany: Springer-Verlag Berlin Heidelberg; 2013.


21. Tsytsarau M, Palpanas T. Survey on mining subjective data on the web. Data Min Knowl Discov. 2012;24(3):478-514.


22. Cambria E, Schuller B, Xia Y, Havasi C. New avenues in opinion mining and sentiment analysis. IEEE Intell Syst. 2013;28(2):15-21.


23. Feldman R. Techniques and applications for sentiment analysis. Commun ACM. 2013;56(4):82-89.


24. Montoyo A, Martínez-Barco P, Balahur A. Subjectivity and sentiment analysis: an overview of the current state of the area and envisaged developments.

Decis Support Syst. 2012;53(4):675-679.


25. Medhat W, Hassan A, Korashy H. Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J. 2014;5(4):1093-1113.


26. Kitchenham B, Charters S. Guidelines for Performing Systematic Literature Reviews in Software Engineering. Technical Report EBSE 2007-001. Keele, UK

and Durham, UK: Keele University and Durham University; 2007.


27. Yerva SR, Miklós Z, Aberer K. Quality-aware similarity assessment for entity matching in Web data. Inf Syst. 2012;37(4):336-351.26(28.)


28. Lou T, Tang J, Hopcroft J, Fang Z, Ding X. Learning to predict reciprocity and triadic closure in social networks. ACM Trans Knowl Discov Data.

2013;7(2):1-25.


29. Arias M, Arratia A, Xuriguera R. Forecasting with Twitter data. ACM Trans Intell Syst Technol. 2013;5(1).


30. Trilla A, Alias F. Sentence-based sentiment analysis for expressive text-to-speech. IEEE Trans Audio Speech Lang Process. 2013;21(2):223-233.


31. Tuarob S, Tucker CS, Salathe M, Ram N. An ensemble heterogeneous classification methodology for discovering health-related knowledge in social

media messages. J Biomed Inform. 2014;49:255-268.


32. Morchid M, Dufour R, Bousquet P-M, Linarès G, Torres-Moreno J-M. Feature selection using principal component analysis for massive retweet

detection. Pattern Recognit Lett. 2014;49:33-39.


33. Montejo-Ráez A, Martínez-Cámara E, Martín-Valdivia MT, Ureña-López LA. Ranked WordNet graph for sentiment polarity classification in Twitter.

Comput Speech Lang. 2014;28(1):93-107.


34. Smailovic J, Gr ́ car M, Lavra ̌ c N, ̌ Znidar ̌ sǐ c M. Stream-based active learning for sentiment analysis in the financial domain. ̌ Inf Sci. 2014;285:181-203.


35. Boella G, Di Caro L, Ruggeri A, Robaldo L. Learning from syntax generalizations for automatic semantic annotation. J Intell Inf Syst.

2014;43(2):231-246.


36. Brynielsson J, Johansson F, Jonsson C, Westling A. Emotion classification of social media posts for estimating people's reactions to communicated

alert messages during crises. Secur Inform. 2014;3:1-11.


37. Arakawa Y, Kameda A, Aizawa A, Suzuki T. Adding Twitter-specific features to stylistic features for classifying tweets by user type and number of

retweets. J Assoc Inf Sci Technol. 2014;65(7):1416-1423.


38. Burnap P, Williams ML, Sloan L, et al. Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack. Soc Netw Anal Min.

2014;4:1-14.


39. Makazhanov A, Rafiei D, Waqar M. Predicting political preference of Twitter users. Soc Netw Anal Min. 2014;4:1-15.


40. Bogdanov P, Busch M, Moehlis J, Singh AK, Szymanski BK. Modeling individual topic-specific behavior and influence backbone networks in social

media. Soc Netw Anal Min. 2014;4:1-16.


41. Lin YR, Margolin D. The ripple of fear, sympathy and solidarity during the Boston bombings. EPJ Data Sci. 2014;3.


42. Fu X, Shen Y. Study of collective user behaviour in Twitter: a fuzzy approach. Neural Comput Appl. 2014;25(7-8):1603-1614.


43. Chen X, Vorvoreanu M, Madhavan K. Mining social media data for understanding students' learning experiences. IEEE Trans Learn Technol.

2014;7(3):246-259.


44. Liu S, Cheng X, Li F, Li F. TASC: topic-adaptive sentiment classification on dynamic tweets. IEEE Trans Knowl Data Eng. 2015;27(6):1696-1709.


45. Kranjc J, Smailovic J, Podpe ́ can V, Gr ̌ car M, ̌ Znidar ̌ sǐ c M, Lavra ̌ c N. Active learning for sentiment analysis on data streams: methodology and workflow ̌

implementation in the ClowdFlows platform. Inf Process Manag. 2015;51(2):187-203.


46. Sluban B, Smailovic J, Battiston S, Mozeti ́ c I. Sentiment leaning of influential communities in social networks. ̌ Comput Soc Netw. 2015;2:1-21.


47. Burnap P, Williams ML. Cyber hate speech on Twitter: an application of machine classification and statistical modeling for policy and decision making.

Policy Internet. 2015;7(2):223-242.


48. Zubiaga A, Spina D, Martinez R, Fresno V. Real-time classification of Twitter trends. J Assoc Inf Sci Technol. 2015;66(3):462-473.


49. Magdy W, Sajjad H, El-Ganainy T, Sebastiani F. Bridging social media via distant supervision. Soc Netw Anal Min. 2015;5:1-12.


50. Tsytsarau M, Palpanas T. Managing diverse sentiments at large scale. IEEE Trans Knowl Data Eng. 2016;28(11):3028-3040.


51. Andriotis P, Oikonomou G, Tryfonas T, Li S. Highlighting relationships of a smartphone's social ecosystem in potentially large investigations. IEEE Trans

Cybern. 2016;46(9):1974-1985.


52. Tang D, Wei F, Qin B, Yang N, Liu T, Zhou M. Sentiment embeddings with applications to sentiment analysis. IEEE Trans Knowl Data Eng.

2016;28(2):496-509.


53. Peetz MH, de Rijke M, Kaptein R. Estimating reputation polarity on microblog posts. Inf Process Manag. 2016;52(2):193-216.


54. Sulis E, Farías DIH, Rosso P, Patti V, Ruffo G. Figurative messages and affect in Twitter: differences between #irony, #sarcasm and #not. Knowl Based

Syst. 2016;108:132-143.


55. Wu F, Song Y, Huang Y. Microblog sentiment classification with heterogeneous sentiment knowledge. Inf Sci. 2016;373:149-164.


56. Lo SL, Chiong R, Cornforth D. Ranking of high-value social audiences on Twitter. Decis Support Syst. 2016;85:34-48.


57. van Zoonen W, Toni GLA. Social media research: the application of supervised machine learning in organizational communication research. Comput

Hum Behav. 2016;63:132-141.


58. Wang Z, Cui X, Gao L, Yin Q, Ke L, Zhang S. A hybrid model of sentimental entity recognition on mobile social media. EURASIP J Wirel Commun Netw.

2016;1:1-12.


59. Celli F, Ghosh A, Alam F, Riccardi G. In the mood for sharing contents: emotions, personality and interaction styles in the diffusion of news. Inf Process

Manag. 2016;52(1):93-98.


60. Igawa RA, Barbon Jr S, Paulo KCS, et al. Account classification in online social networks with LBCA and wavelets. Inf Sci. 2016;332:72-83.


61. Korkmaz G, Cadena J, Kuhlman CJ, Marathe A, Vullikanti A, Ramakrishnan N. Multi-source models for civil unrest forecasting. Soc Netw Anal Min.

2016;6:1-25.


62. Burnap P, Williams ML. Us and them: identifying cyber hate on Twitter across multiple protected characteristics. EPJ Data Sci. 2016;5:1-15.


63. Oliveira N, Cortez P, Areal N. The impact of microblogging data for stock market prediction: using Twitter to predict returns, volatility, trading volume

and survey sentiment indices. Expert Syst Appl. 2017;73:125-144.


64. Perikos I, Hatzilygeroudis I. Recognizing emotions in text using ensemble of classifiers. Eng Appl Artif Intell. 2016;51:191-201.


65. Brocardo ML, Traore I, Woungang I, Obaidat MS. Authorship verification using deep belief network systems. Int J Commun Syst. 2017;30(12):1-10.


66. Bouazizi M, Ohtsuki TO. A pattern-based approach for sarcasm detection on Twitter. IEEE Access. 2016;4:5477-5488.


67. Farías DIH, Patti V, Rosso P. Irony detection in Twitter: the role of affective content. ACM Trans Internet Technol. 2016;16(3):1-24.


68. Sintsova V, Pu P. Dystemo: distant supervision method for multi-category emotion recognition in tweets. ACM Trans Intell Syst Technol. 2016;8(1):1-22.


69. Nair LR, Shetty SD, Shetty SD. Applying spark based machine learning model on streaming big data for health status prediction. Comput Electr Eng.

2018;65:393-399.


70. Cui L, Zhang X, Qin AK, Sellis T, Wu L. CDS: collaborative distant supervision for Twitter account classification. Expert Syst Appl. 2017;83:94-103.


71. Pérez-Gállego P, Quevedo JR, del Coz JJ. Using ensembles for problems with characterizable changes in data distribution: a case study on quantification.

Inf Fusion. 2017;34:87-100.


72. Alsinet T, Argelich J, Béjar R, Fernández C, Mateu C, Planes J. Weighted argumentation for analysis of discussions in Twitter. Int J Approx Reason.

2017;85:21-35.


73. Jianqiang Z, Xiaolin G. Comparison research on text pre-processing methods on Twitter sentiment analysis. IEEE Access. 2017;5:2870-2879.


74. Jain VK, Kumar S. Effective surveillance and predictive mapping of mosquito-borne diseases using social media. J Comput Sci. 2018;25:406-415.


75. Keshavarz H, Abadeh MS. ALGA: adaptive lexicon learning using genetic algorithm for sentiment analysis of microblogs. Knowl Based Syst.

2017;122:1-16.


76. Xiong S, Lv H, Zhao W, Ji D. Towards Twitter sentiment classification by multi-level sentiment-enriched word embeddings. Neurocomputing.

2018;275:2459-2466.


77. Neppalli VK, Caragea C, Squicciarini A, Tapia A, Stehle S. Sentiment analysis during Hurricane Sandy in emergency response. Int J Disaster Risk Reduct.

2018;21:213-222.


78. Singh A, Shukla N, Mishra N. Social media data analytics to improve supply chain management in food industries. Transp Res Part E Logist Transp Rev.

2018;114:398-415.


79. Xiaomei Z, Jing Y, Jianpei Z, Hongyu H. Microblog sentiment analysis with weak dependency connections. Knowl Based Syst. 2018;142:170-180.


80. Khan I, Naqvi SK, Alam M, Rizvi SNA. An efficient framework for real-time tweet classification. Int J Inf Technol. 2017;9(2):215-221.


81. Bouazizi M, Ohtsuki T. A pattern-based approach for multi-class sentiment analysis in Twitter. IEEE Access. 2017;5:20617-20639.


82. Li B, Chan KC, Ou C, Ruifeng S. Discovering public sentiment in social media for predicting stock movement of publicly listed companies. Inf Syst.

2017;69:81-92.


83. Jianqiang Z, Xiaolin G, Xuejun Z. Deep convolution neural networks for Twitter sentiment analysis. IEEE Access. 2018;6:23253-23260.


84. Ghiassi M, Lee S. A domain transferable lexicon set for Twitter sentiment analysis using a supervised machine learning approach. Expert Syst Appl.

2018;106:197-216.


85. Symeonidis S, Effrosynidis D, Arampatzis A. A comparative evaluation of pre-processing techniques and their interactions for Twitter sentiment

analysis. Expert Syst Appl. 2018;110:298-310.


https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.5107


No comments: