Systematic Literature Review of Sentiment Analysis Techniques

Authors

  • Manuel Hilario
  • Doris Esenarro
  • Ivan Petrlik
  • Ciro Rodriguez

Keywords:

Natural Language Processing, Opinion Mining, Sentence Classification, Sentiment Analysis.

Abstract

Sentiment analysis has become an important research area that aims to understand people's opinions by analyzing a large size of information. There are two types of sentiment analysis methods: those based on the lexicon and those based on Machine Learning algorithms. Although there are many proposals related to sentiment analysis, there is still a great margin for improving results. The objective of this work is to identify the current state of the latest research related to the analysis of feelings, making use of a framework for the systematic review of the literature, in order to answer the following research questions: RQ1 What are the types of methods used for sentiment analysis? RQ2 What kind of data sources are used to perform sentiment analysis? A crossover analysis of the results was performed. One of the results showed that the most used classifier was Naïve Bayes. Besides, most of the works reviewed used texts extracted from microblogs, web pages, E-Commerce, and other data sources, to perform sentiment analysis.

References

Bustamante, J.C., Rodriguez, C., Esenarro, D. (2019). Real time facial expression recognition system based on deep learning. International Journal of Recent Technology and Engineering, 2019, 8(2 Special Issue 11), pp. 4047-4051.

doi: 10.35940/ijrte.B1591.0982S1119

B. A. Kitchenham and S. Charters. (2007). Guidelines for performing systematic literature reviews in software engineering version 2.3. Retrieved January 9, 2014, from http://www.elsevier.com/ data/promis_misc/525444systematicreviewsguide.pdf.

A. Salinca, "Business Reviews Classification Using Sentiment Analysis," 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timisoara, 2015, pp. 247-250, doi: 10.1109/SYNASC.2015.46.

Y. Woldemariam, "Sentiment analysis in a cross-media analysis framework," 2016 IEEE International Conference on Big Data Analysis (ICBDA), Hangzhou, 2016, pp. 1-5, doi: 10.1109/ICBDA.2016.7509790.

Q. Li, S. Shah, R. Fang, A. Nourbakhsh and X. Liu, "Tweet Sentiment Analysis by Incorporating Sentiment-Specific Word Embedding and Weighted Text Features," 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Omaha, NE, 2016, pp. 568-571, doi: 10.1109/WI.2016.0097.

R. Jose and V. S. Chooralil, "Prediction of election result by enhanced sentiment analysis on twitter data using classifier ensemble Approach," 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), Ernakulam, 2016, pp. 64-67, doi: 10.1109/SAPIENCE.2016.7684133.

J. Li and L. Qiu, "A Sentiment Analysis Method of Short Texts in Microblog," 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), Guangzhou, 2017, pp. 776-779, doi: 10.1109/CSE-EUC.2017.153.

M. Maia, A. Freitas, and S. Handschuh, "FinSSLx: A Sentiment Analysis Model for the Financial Domain Using Text Simplification," 2018 IEEE 12th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, 2018, pp. 318-319, doi: 10.1109/ICSC.2018.00065.

E. M. Alshari, A. Azman, S. Doraisamy, N. Mustapha and M. Alkeshr, "Effective Method for Sentiment Lexical Dictionary Enrichment Based on Word2Vec for Sentiment Analysis," 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP), Kota Kinabalu, 2018, pp. 1-5, doi: 10.1109/INFRKM.2018.8464775.

K. Zvarevashe and O. O. Olugbara, "A framework for sentiment analysis with opinion mining of hotel reviews," 2018 Conference on Information Communications Technology and Society (ICTAS), Durban, 2018, pp. 1-4, doi: 10.1109/ICTAS.2018.8368746.

B. Sun, F. Tian and L. Liang, "Tibetan Micro-Blog Sentiment Analysis Based on Mixed Deep Learning," 2018 International Conference on Audio, Language, and Image Processing (ICALIP), Shanghai, 2018, pp. 109-112, doi: 10.1109/ICALIP.2018.8455328.

R. Bandana, "Sentiment Analysis of Movie Reviews Using Heterogeneous Features," 2018 2nd International Conference on Electronics, Materials Engineering & Nano- Technology (IEMENTech), Kolkata, 2018, pp. 1-4, doi: 10.1109/IEMENTECH.2018.8465346.

M. Wongkar and A. Angdresey, "Sentiment Analysis Using Naive Bayes Algorithm Of The Data Crawler: Twitter," 2019 Fourth International Conference on Informatics and Computing (ICIC), Semarang, Indonesia, 2019, pp. 1-5, doi: 10.1109/ICIC47613.2019.8985884.

T. Rabeya, N. R. Chakraborty, S. Ferdous, M. Dash, and A. Al Marouf, "Sentiment Analysis of Bangla Song Review- A Lexicon Based Backtracking Approach," 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 2019, pp. 1-7, doi: 10.1109/ICECCT.2019.8869290.

J. S. Lee, D. Zuba and Y. Pang, "Sentiment Analysis of Chinese Product Reviews using Gated Recurrent Unit," 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), Newark, CA, USA, 2019, pp. 173-181, doi: 10.1109/BigDataService.2019.00030.

L. Cheng and S. Tsai, "Deep Learning for Automated Sentiment Analysis of Social Media," 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Vancouver, BC, Canada, 2019, pp. 1001-1004, doi: 10.1145/3341161.3344821.

M. Rumelli, D. Akkuş, Ö. Kart and Z. Isik, "Sentiment Analysis in Turkish Text with Machine Learning Algorithms," 2019 Innovations in Intelligent Systems and Applications Conference (ASYU), Izmir, Turkey, 2019, pp. 1-5, doi: 10.1109/ASYU48272.2019.8946436.

A. Poornima and K. S. Priya, "A Comparative Sentiment Analysis Of Sentence Embedding Using Machine Learning Techniques," 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2020, pp. 493-496, doi: 10.1109/ICACCS48705.2020.9074312.

L. Yang, Y. Li, J. Wang and R. S. Sherratt, "Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning," in IEEE Access, vol. 8, pp. 23522-23530, 2020, doi: 10.1109/ACCESS.2020.2969854.

H. AlSalman, "An Improved Approach for Sentiment Analysis of Arabic Tweets in Twitter Social Media," 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, Saudi Arabia, 2020, pp. 1-4, doi: 10.1109/ICCAIS48893.2020.9096850.

Huapaya, H. D., Rodriguez, C., y Esenarro, D. (2020). Comparative analysis of supervised machine learning algorithms for heart disease detection. 3C Tecnología. Glosas de innovación aplicadas a la pyme. Edición Especial, Abril 2020, 233-247. http://doi.org/10.17993/3ctecno.2020. specialissue5.233-247

Rodriguez C., Angeles, D., Chafloque R., Kaseng F. and Pandey B., "Deep Learning Audio Spectrograms Processing to the Early COVID-19 Detection," 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), Bhimtal, India, 2020, pp. 429-434, doi: 10.1109/CICN49253.2020.9242583.

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Published

2021-02-28

How to Cite

Hilario, M. ., Esenarro, D. ., Petrlik, I. ., & Rodriguez, C. . (2021). Systematic Literature Review of Sentiment Analysis Techniques. The Journal of Contemporary Issues in Business and Government, 27(1), 506–517. Retrieved from https://cibgp.com/au/index.php/1323-6903/article/view/582

Issue

Vol. 27 No. 1 (2021): The journal of contemporary issues in business and government

Section

Articles

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