Monday, December 31, 2018

Sentiment analysis for Malay language: Systematic literature review

Abstract

Recent research and developments in Sentiment Analysis (SA) have simplified sentiment detection and classification from textual content. 

The related domains for these studies are diverse and comprise fields such as tourism, costumer review, finance, software engineering, speech conversation, social media content, news and so on. 

SA research and developments field have been done on various languages such as Chinese and English language. 

However, SA research on other languages such as Malay language is still scarce. 

Thus, there is a need for constructing SA research specifically for Malay language. 

To understand trends and to support practitioners and researchers with comprehension information with regard to SA for Malay language, this study exhibit to review published articles on SA for Malay language. 

From five online databases including ACM, Emerald insight, IEEE Xplore, Science Direct, and Scopus, 2433 scientific articles were obtained. 

Moreover, through the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement, 10 articles have been chosen for the review process. 

Those articles have been reviewed depend on a few categories consisting of the aim of the study, SA classification techniques, as well as the domain and source of content. 

As a result, the conducted systematic literature review shed some light about the starting point to research in term of SA for Malay language.

REFERENCES

[1] S. Maghilnan and M. R. Kumar, “Sentiment Analysis on Speaker

Specific Speech Data,” 2017 Int. Conf. Intell. Comput. Control, 2018.


[2] S. Ahuja and G. Dubey, “Clustering and Sentiment Analysis on Twitter

Data,” 2017 2nd Int. Conf. Telecommun. Networks (TEL-NET 2017),

vol. 2, no. 1, pp. 178–184, 2017.


[3] J. Vora and A. M. Chacko, “Sentiment Analysis of Tweets to Identify

the Correlated Factors That Influence an Issue of Interest,” 2017 2nd Int.

Conf. Telecommun. Networks (TEL-NET 2017), 2017.


[4] S. Kumar and R. Singh, “Comparative analysis of ensemble classifiers

for sentiment analysis and opinion mining,” 2017 3rd Int. Conf. Adv.

Comput. Autom., pp. 1–5, 2017.


[5] Y. H. Gu, S. J. Yoo, Z. Jiang, Y. J. Lee, Z. Piao, H. Yin, and S. Jeon,

“Sentiment analysis and visualization of Chinese tourism blogs and

reviews,” 2018 Int. Conf. Electron. Information, Commun., 2018.


[6] Y. Fang, H. Tan, and J. Zhang, “Multi-Strategy Sentiment Analysis of

Consumer Reviews Based on Semantic Fuzziness,” IEEE Access, vol. 6,

pp. 20625–20631, 2018.


[7] M. E. Basiri and A. Kabiri, “Translation is Not Enough: Comparing

Lexicon-based Methods for Sentiment Analysis in Persian,” 2017 Int.

Symp. Comput. Sci. Softw. Eng. Conf., no. Ml, pp. 36–41, 2017.


[8] Z. Singla, S. Randhawa, and S. Jain, “Sentiment Analysis of Customer

Product Reviews Using Machine Learning,” 2017 Int. Conf. Intell.

Comput. Control, 2017.


[9] S. Sohangir, N. Petty, and D. Wang, “Financial Sentiment Lexicon

Analysis,” 2018 12th IEEE Int. Conf. Semant. Comput., pp. 0–3, 2018.


[10] P. Chakraborty, U. S. Pria, M. R. A. H. Rony, and M. A. Majumdar,

“Predicting Stock Movement using Sentiment Analysis of Twitter

Feed,” 2017 6th Int. Conf. Informatics, Electron. Vis. 2017 7th Int.

Symp. Comput. Med. Heal. Technol., pp. 1–6, 2017.


[11] W. Zongyue and Q. Sujuan, “A Sentiment Analysis Method of Chinese

Specialized Field Short Commentary,” 2017 3rd IEEE Int. Conf.

Comput. Commun., 2017.


[12] M. R. Islam and M. F. Zibran, “A Comparison of Software Engineering

Domain Specific Sentiment Analysis Tools,” SANER 2018, no. 978, pp.

487–491, 2018.


[13] D. K. Tayal and S. K. Yadav, “Analysis of Sentiments & Polarity

Computation of Opinions,” 2017 2nd Int. Conf. Telecommun. Networks

(TEL-NET 2017), pp. 2–7, 2017.


[14] Y. Li and B. Shen, “Research on Sentiment Analysis of Microblogging

Based on LSA and TF-IDF,” 2017 3rd IEEE Int. Conf. Comput.

Commun., 2017.


[15] K. Lavanya and C. Deisy, “Twitter Sentiment Analysis Using Multi-

Class SVM,” 2017 Int. Conf. Intell. Comput. Control, 2017.


[16] T. S. K. Chaitanya, V. S. Harika, and B. Prabadevi, “A sentiment

analysis approach by identifying the subject object relationship,” 2017

2nd Int. Conf. Commun. Electron. Syst., no. Icces, pp. 62–68, 2017.


[17] N. Bhan and M. D’silva, “Sarcasmometer using Sentiment Analysis and

Topic Modeling,” 2017 Int. Conf. Adv. Comput. Commun. Control, pp.

1–7, 2017.


[18] F. S. Al-anzi and D. AbuZeina, “A Micro-Word based Approach for

Arabic Sentiment Analysis,” 2017 IEEE/ACS 14th Int. Conf. Comput.

Syst. Appl., pp. 910–914, 2017.


[19] D. Abuaiadah, D. Rajendran, and M. Jarrar, “Clustering Arabic Tweets

for Sentiment Analysis,” 2017 IEEE/ACS 14th Int. Conf. Comput. Syst.

Appl., pp. 449–456, 2017.


[20] Y. Liang, B. Fu, and Z. Li, “Sentiment Tendency Analysis of THAAD

event in Indonesian News,” 2017 14th Web Inf. Syst. Appl. Conf., pp.

211–214, 2017.


[21] Y. Sharma, G. Agrawal, P. Jain, and T. Kumar, “Vector Representation

of Words for Sentiment Analysis Using GloVe,” 2017 Int. Conf. Intell.

Commun. Comput. Tech., pp. 279–284, 2017.


[22] A. A. Aziz, A. Starkey, and M. C. Bannerman, “Evaluating Cross

Domain Sentiment Analysis using Supervised Machine Learning

Techniques,” Intell. Syst. Conf. 2017, no. September, pp. 689–696,

2017.


[23] D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, “Preferred

reporting items for systematic reviews and meta-analyses: the PRISMA

statement.,” PLoS Med., vol. 6, no. 7, p. e1000097, Jul. 2009.


[24] N. Isa, M. Puteh, and R. M. H. R. M. H. R. Kamarudin, “Sentiment

classification of malay newspaper using immune network (SCIN),”

Proc. World Congr. Eng., vol. 3, pp. 1543–1548, 2013.


[25] A. Alsaffar and N. Omar, “Study on feature selection and machine

learning algorithms for Malay sentiment classification,” Conf. Proc. -

6th Int. Conf. Inf. Technol. Multimed. UNITEN Cultiv. Creat. Enabling

Technol. Through Internet Things, ICIMU 2014, pp. 270–275, 2014.


[26] A. Alsaffar and N. Omar, “Integrating a Lexicon based approach and K

nearest neighbour for Malay sentiment analysis,” J. Comput. Sci., vol.

11, no. 4, pp. 639–644, 2015.


[27] Y. F. Tan, H. S. Lam, A. Azlan, and W. K. Soo, “Sentiment analysis for

telco popularity on twitter big data using a novel Malaysian dictionary,”

Front. Artif. Intell. Appl., vol. 282, pp. 112–125, 2016.


[28] S. S. Hasbullah, D. Maynard, R. Z. W. Chik, F. Mohd, and M. Noor,

“Automated Content Analysis: A Sentiment Analysis on Malaysian

Government Social Media,” in IMCOM ’16: Proceedings of the 10th

International Conference on Ubiquitous Information Management and

Communication, 2016, p. 30:1--30:6.


[29] M. Darwich, S. A. M. Noah, and N. Omar, “Minimally-supervised

sentiment lexicon induction model: A case study of malay sentiment

analysis,” Multi-disciplinary Trends Artif. Intell., vol. 10607, no.

November, pp. 225–237, 2017.


[30] T. Al-Moslmi, N. Omar, M. Albared, and A. Alshabi, “Enhanced Malay

Sentiment Analysis with an Ensemble Classification Machine Learning

Approach,” Journal of Engineering and Applied Sciences, vol. 12, no.

20. pp. 5226–5232, 2017.


[31] N. A. Nasharuddin, M. T. Abdullah, A. Azman, and R. A. Kadir,

“English and Malay cross-lingual sentiment lexicon acquisition and

analysis,” vol. 424, p. 4154, 2017.


[32] K. Chekima and R. Alfred, “Sentiment Analysis of Malay Social Media

Text,” in Computational Science and Technology, 2018, pp. 205–219.


[33] K. Chekima, Rayner Alfred, and K. O. Chin, “Rule-Based Model for

Malay Text Sentiment Analysis,” Int. Conf. Comput. Sci. Technol.

ICCST 2017 Comput. Sci. Technol., pp. 172–185, 2018.



https://ieeexplore.ieee.org/abstract/document/8567139

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