Survey Opinion using Sentiment Analysis

Taqwa Hariguna, Husni Teja Sukmana, Jong Il Kim

Abstract


Sentiment analysis or opinion mining is a computational study of the opinions, judgments, attitudes, and emotions of a person towards an entity, individual, issue, event, topic, and attributes. This task is very challenging technically but very useful in practice. For example, a business always wants to seek opinion about its products and services from the public or the consumers. Additionally, potential consumers want to learn what users think they have when using a service or purchasing a product. To get public opinion on food habits, ad strategies, political trends, social issues and business policy, this is a very critical factor. This paper will explain a survey of key sentiment-extraction approaches.


Keywords


Opinion mining; Business; Machine learning; Sentiment Analysis; Survey

Full Text:

PDF

References


[1]    M. Rushdi Saleh, M. T. Martín-Valdivia, A. Montejo-Ráez, and L. A. Ureña-López, “Experiments with SVM to classify opinions in different domains,” Expert Syst. Appl., vol. 38, no. 12, pp. 14799–14804, 2011, doi: 10.1016/j.eswa.2011.05.070.

[2]    W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Eng. J., vol. 5, no. 4, pp. 1093–1113, 2014, doi: 10.1016/j.asej.2014.04.011.

[3]    A. Montoyo, P. Martínez-Barco, and A. Balahur, “Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments,” Decis. Support Syst., vol. 53, no. 4, pp. 675–679, 2012, doi: 10.1016/j.dss.2012.05.022.

[4]    J. Bollen, H. Mao, and X. Zeng, “Twitter mood predicts the stock market,” J. Comput. Sci., vol. 2, no. 1, pp. 1–8, 2011, doi: 10.1016/j.jocs.2010.12.007.

[5]    F. Greco and A. Polli, “Emotional Text Mining: Customer profiling in brand management,” Int. J. Inf. Manage., vol. 51, no. December 2018, pp. 1–8, 2020, doi: 10.1016/j.ijinfomgt.2019.04.007.

[6]    J. Jeong, P. Kim, and Y. Jeong, “Harmonics Suppressed Band-pass Matching Network for High Efficiency Power Amplifier,” no. 2, pp. 280–283, 2017.

[7]    P. Pirolli and S. Card, “Information foraging,” Psychol. Rev., vol. 106, no. 4, pp. 643–675, 1999, doi: 10.1037/0033-295X.106.4.643.

[8]    B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment Classification using Machine Learning Techniques,” 2002, doi: 10.3115/1118693.1118704.

[9]    M. Araújo, A. Pereira, and F. Benevenuto, “A comparative study of machine translation for multilingual sentence-level sentiment analysis,” Inf. Sci. (Ny)., vol. 512, pp. 1078–1102, 2020, doi: 10.1016/j.ins.2019.10.031.

[10]    A. S. Hosseini, “Sentence-level emotion mining based on combination of adaptive Meta-level features and sentence syntactic features,” Eng. Appl. Artif. Intell., vol. 65, pp. 361–374, 2017, doi: 10.1016/j.engappai.2017.08.006.

[11]    E. Cambria, R. Speer, C. Havasi, and A. Hussain, “SenticNet: A publicly available semantic resource for opinion mining,” AAAI Fall Symp. - Tech. Rep., vol. FS-10-02, pp. 14–18, 2010.

[12]    Jindal, Nitin and Bing Liu. “Identifying comparative sentences in text documents.” in Proceedings of ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR-2006), pp. 244-251, August 2006. doi: 10.1145/1148170.1148215

[13]    N. Jindal and B. Liu, “Mining comparative sentences and relations,” Proc. Natl. Conf. Artif. Intell., vol. 2, pp. 1331–1336, 2006.

[14]    S. Yang and Y. Ko, “Extracting comparative entities and predicates from texts using comparative type classification,” ACL-HLT 2011 - Proc. 49th Annu. Meet. Assoc. Comput. Linguist. Hum. Lang. Technol., vol. 1, pp. 1636–1644, 2011.




DOI: https://doi.org/10.47738/jads.v1i1.10

Refbacks

  • There are currently no refbacks.



Barcode

Journal of Applied Data Sciences

ISSN:2723-6471 (Online)
Publisher:Bright Publisher
Website:http://bright-journal.org/JADS
Email:taqwa@amikompurwokerto.ac.id (principal contact)
  support@bright-journal.org (technical issues)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0