Abstract
Sentiment analysis also referred to as opinion mining, is an automated process for identifying and classifying subjective information such as sentiments from a piece of text usually comments and reviews.
Supported by machine learning algorithms, it is possible to identify positive, neutral or negative opinions, being possible to rank or classify them in order to reach some kind of conclusion or obtain any type of information.
Thus, this paper aims to perform a systematic literature review in order to report the state-of-the-art of machine learning techniques for sentiment analysis applied to texts of reviews, comments and evaluations of scientific papers.
REFERENCES
Abbasi, A., Chen, H., & Salem, A. (2008). Sentiment analysis in multiple languages. ACM
Transactions on Information Systems, 26(3), 1–34. https://doi.org/10.1145/1361684.1361685
Afzaal, M., Usman, M., Fong, A. C. M., & Fong, S. (2019). Multiaspect-based opinion classification
model for tourist reviews. Expert Systems, e12371. https://doi.org/10.1111/exsy.12371
Al-amrani, Y., Lazaar, M., Eddine, K., & Kadiri, E. L. (2018). Sentiment Analysis Using Hybrid
Method of. Journal of Theoretical and Applied Information Technology, 96(7), 1886–1895.
Retrieved from www.jatit.org
Araque, O., Corcuera-Platas, I., Sánchez-Rada, J. F., & Iglesias, C. A. (2017). Enhancing deep
learning sentiment analysis with ensemble techniques in social applications. Expert Systems
with Applications, 77, 236–246. https://doi.org/10.1016/j.eswa.2017.02.002
Baek, H., Ahn, J., & Choi, Y. (2012). Helpfulness of Online Consumer Reviews: Readers’ Objectives
and Review Cues. International Journal of Electronic Commerce, 17(2), 99–126.
https://doi.org/10.2753/jec1086-4415170204
Boudad, N., Faizi, R., Oulad Haj Thami, R., & Chiheb, R. (2018). Sentiment analysis in Arabic: A
review of the literature. Ain Shams Engineering Journal, 9(4), 2479–2490.
https://doi.org/10.1016/j.asej.2017.04.007
Choi, D., Ko, B., Kim, H., & Kim, P. (2014). Text analysis for detecting terrorism-related articles on
the web. Journal of Network and Computer Applications, 38(1), 16–21.
https://doi.org/10.1016/j.jnca.2013.05.007
Do, H. H., Prasad, P. W. C., Maag, A., & Alsadoon, A. (2019). Deep Learning for Aspect-Based
Sentiment Analysis: A Comparative Review. Expert Systems with Applications, 118, 272–299.
https://doi.org/10.1016/j.eswa.2018.10.003
Erdt, M., Nagarajan, A., Sin, S. C. J., & Theng, Y. L. (2016). Altmetrics: an analysis of the state-of-
the-art in measuring research impact on social media. Scientometrics, 109(2), 1117–1166.
https://doi.org/10.1007/s11192-016-2077-0
Fernández-Gavilanes, M., Àlvarez-López, T., Juncal-Martínez, J., Costa-Montenegro, E., &
González-Castaño, F. J. (2015). GTI: An Unsupervised Approach for Sentiment Analysis in
Twitter (pp. 533–538). https://doi.org/10.18653/v1/s15-2089
Fortuna, P., & Nunes, S. (2018). A Survey on Automatic Detection of Hate Speech in Text. ACM
Computing Surveys, 51(4), 1–30. https://doi.org/10.1145/3232676
Ganu, G. (2009). Beyond the Stars : Improving Rating Predictions using Review Text Content. Text,
1–6. Retrieved from http://www.dbmi.columbia.edu/noemie/ursa
García-Pablos, A., Cuadros, M., & Rigau, G. (2018). W2VLDA: Almost unsupervised system for
Aspect Based Sentiment Analysis. Expert Systems with Applications, 91, 127–137.
https://doi.org/10.1016/j.eswa.2017.08.049
Heydari, A., Tavakoli, M. ali, Salim, N., & Heydari, Z. (2015). Detection of review spam: A survey.
Expert Systems with Applications, 42(7), 3634–3642.
https://doi.org/10.1016/J.ESWA.2014.12.029
Hoon, L., Vasa, R., Schneider, J.-G., & Mouzakis, K. (2012). A preliminary analysis of vocabulary in
mobile app user reviews. In Proceedings of the 24th Australian Computer-Human Interaction
Conference on - OzCHI ’12 (pp. 245–248). New York, New York, USA: ACM Press.
https://doi.org/10.1145/2414536.2414578
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the 2004
ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’04
(p. 168). New York, New York, USA: ACM Press. https://doi.org/10.1145/1014052.1014073
Lei, P., Marfia, G., Pau, G., & Tse, R. (2018). Can we monitor the natural environment analyzing
online social network posts? A literature review. Online Social Networks and Media, 5, 51–60.
https://doi.org/10.1016/j.osnem.2017.12.001
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language
Technologies, 5(1), 1–167. https://doi.org/10.2200/S00416ED1V01Y201204HLT016
Mäntylä, M. V., Graziotin, D., & Kuutila, M. (2018). The evolution of sentiment analysis—A review
of research topics, venues, and top cited papers. Computer Science Review, 27, 16–32.
https://doi.org/10.1016/j.cosrev.2017.10.002
Mirończuk, M. M., & Protasiewicz, J. (2018). A recent overview of the state-of-the-art elements of
text classification. Expert Systems with Applications, 106, 36–54.
https://doi.org/10.1016/j.eswa.2018.03.058
Moussa, M. E., Mohamed, E. H., & Haggag, M. H. (2018). A survey on opinion summarization
techniques for social media. Future Computing and Informatics Journal, 3(1), 82–109.
https://doi.org/10.1016/j.fcij.2017.12.002
Musto, C., Semeraro, G., & Polignano, M. (2014). A comparison of lexicon-based approaches for
sentiment analysis of microblog. In CEUR Workshop Proceedings (Vol. 1314, pp. 59–68).
Retrieved from http://ceur-ws.org/Vol-1314/paper-06.pdf
Popescu, A. M., & Etzioni, O. (2007). Extracting product features and opinions from reviews. In
Natural Language Processing and Text Mining (pp. 9–28). London: Springer London.
https://doi.org/10.1007/978-1-84628-754-1_2
Rambocas, M., & Gama, J. (2013). Marketing Research: The Role of Sentiment Analysis. Retrieved
from https://pdfs.semanticscholar.org/acd0/c9f75152acd2a622be442d20f96b0a3225d4.pdf
Sabbah, T., Selamat, A., Selamat, M. H., Ibrahim, R., & Fujita, H. (2016). Hybridized term-weighting
method for Dark Web classification. Neurocomputing, 173, 1908–1926.
https://doi.org/10.1016/j.neucom.2015.09.063
Sammut, C., & Webb, G. I. (Eds.). (2010). Encyclopedia of Machine Learning. Boston, MA: Springer
US. https://doi.org/10.1007/978-0-387-30164-8
Sundermann, C., Domingues, M., Sinoara, R., Marcacini, R., & Rezende , S. (2019). Using Opinion
Mining in Context-Aware Recommender Systems: A Systematic Review. Information, 10(2),
42. https://doi.org/10.3390/info10020042
Tavakoli, M., Zhao, L., Heydari, A., & Nenadić, G. (2018). Extracting useful software development
information from mobile application reviews: A survey of intelligent mining techniques and
tools. Expert Systems with Applications, 113, 186–199.
https://doi.org/10.1016/j.eswa.2018.05.037
Ware, M., & Mabe, M. (2015). The STM Report: An overview of scientific and scholarly journal
publishing. Copyright, Fair Use, Scholarly Communication, Etc.
Webster, J., & Watson, R. T. (2002). Analyzing the Past To Prepare for the Future : Writing a
Literature Review. MIS Quarterly, 26(2), xiii–xxiii.
Zimbra, D., Abbasi, A., Zeng, D., & Chen, H. (2018). The State-of-the-Art in Twitter Sentiment
Analysis. ACM Transactions on Management Information Systems, 9(2), 1–29.
https://doi.org/10.1145/3185045
https://repositorium.sdum.uminho.pt/handle/1822/65115
