Issue

Vol. 5, No. 3, August 2020

Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

https://doi.org/10.22219/kinetik.v5i3.1066
Farrikh Alzami
Universitas Dian Nuswantoro, Semarang
https://orcid.org/0000-0003-2669-3864
Erika Devi Udayanti
Universitas Dian Nuswantoro, Semarang
Dwi Puji Prabowo
Universitas Dian Nuswantoro, Semarang
Rama Aria Megantara
Universitas Dian Nuswantoro, Semarang

Corresponding Author(s) : Farrikh Alzami

alzami@dsn.dinus.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 5, No. 3, August 2020

Abstract

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

Keywords

Unstructured Sentiment Analysis polarity TF-IDF classification
Alzami, F., Udayanti, E. D., Prabowo, D. P., & Megantara, R. A. (2020). Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 5(3), 235-242. https://doi.org/10.22219/kinetik.v5i3.1066
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References
  1. Agarwal, B., Mittal, N., Bansal, P., & Garg, S. (2015). Sentiment Analysis Using Common-Sense and Context Information. Computational Intelligence and Neuroscience, 2015, 1–9. https://doi.org/10.1155/2015/715730
  2. Cambria, E., Hussain, A., Durrani, T., Havasi, C., Eckl, C., & Munro, J. (2010). Sentic Computing for patient centered applications. IEEE 10th International Conference On Signal Processing Proceedings, 1279–1282. https://doi.org/10.1109/ICOSP.2010.5657072
  3. Ebrahimi, M., Yazdavar, A. H., & Sheth, A. (2017). Challenges of Sentiment Analysis for Dynamic Events. IEEE Intelligent Systems, 32(5), 70–75. https://doi.org/10.1109/MIS.2017.3711649
  4. Xing, F. Z., Cambria, E., & Welsch, R. E. (2018). Natural language based financial forecasting: a survey. Artificial Intelligence Review, 50(1), 49–73. https://doi.org/10.1007/s10462-017-9588-9
  5. Van de Kauter, M., Breesch, D., & Hoste, V. (2015). Fine-grained analysis of explicit and implicit sentiment in financial news articles. Expert Systems with Applications, 42(11), 4999–5010. https://doi.org/10.1016/j.eswa.2015.02.007
  6. Valdivia, A., Luzon, M. V., & Herrera, F. (2017). Sentiment Analysis in TripAdvisor. IEEE Intelligent Systems, 32(4), 72–77. https://doi.org/10.1109/MIS.2017.3121555
  7. Vázquez, S., Muñoz-García, Ó., Campanella, I., Poch, M., Fisas, B., Bel, N., & Andreu, G. (2014). A classification of user-generated content into consumer decision journey stages. Neural Networks, 58, 68–81. https://doi.org/10.1016/j.neunet.2014.05.026
  8. Thompson, J. J., Leung, B. H., Blair, M. R., & Taboada, M. (2017). Sentiment analysis of player chat messaging in the video game StarCraft 2: Extending a lexicon-based model. Knowledge-Based Systems, 137, 149–162. https://doi.org/10.1016/j.knosys.2017.09.022
  9. Wang, K., Liu, X., & Han, Y. (2019). Exploring Goodreads reviews for book impact assessment. Journal of Informetrics, 13(3), 874–886. https://doi.org/10.1016/j.joi.2019.07.003
  10. Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45–59. https://doi.org/10.1016/j.inffus.2015.08.005
  11. Elghannam, F. (2019). Text representation and classification based on bi-gram alphabet. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2019.01.005
  12. Chalothorn, T., & Ellman, J. (2015). Simple approaches of sentiment analysis via ensemble learning. In Lecture Notes in Electrical Engineering (Vol. 339, pp. 631–639). https://doi.org/10.1007/978-3-662-46578-3_74
  13. Yang, L., Li, Y., Wang, J., & Sherratt, R. S. (2020). Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning. IEEE Access, 8, 23522–23530. https://doi.org/10.1109/ACCESS.2020.2969854
  14. Zeng, D., Dai, Y., Li, F., Wang, J., & Sangaiah, A. K. (2019). Aspect based sentiment analysis by a linguistically regularized CNN with gated mechanism. Journal of Intelligent & Fuzzy Systems, 36(5), 3971–3980. https://doi.org/10.3233/JIFS-169958
  15. Khan, K., Baharudin, B., Khan, A., & Ullah, A. (2014). Mining opinion components from unstructured reviews: A review. Journal of King Saud University - Computer and Information Sciences, 26(3), 258–275. https://doi.org/10.1016/j.jksuci.2014.03.009
  16. Hussein, D. M. E.-D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University - Engineering Sciences, 30(4), 330–338. https://doi.org/10.1016/j.jksues.2016.04.002
  17. Moraes, R., Valiati, J. F., & Gavião Neto, W. P. (2013). Document-level sentiment classification: An empirical comparison between SVM and ANN. Expert Systems with Applications, 40(2), 621–633. https://doi.org/10.1016/j.eswa.2012.07.059
  18. McAuley, J., & Leskovec, J. (2013). From amateurs to connoisseurs: Modeling the evolution of user expertise through online reviews. WWW 2013 - Proceedings of the 22nd International Conference on World Wide Web, 897–907. Retrieved from https://arxiv.org/abs/1303.4402
  19. Willett, P. (2006). The Porter stemming algorithm: then and now. Program, 40(3), 219–223. https://doi.org/10.1108/00330330610681295
  20. Xie, F., Wu, X., & Zhu, X. (2017). Efficient sequential pattern mining with wildcards for keyphrase extraction. Knowledge-Based Systems, 115, 27–39. https://doi.org/10.1016/j.knosys.2016.10.011
  21. Gencosman, B. C., Ozmutlu, H. C., & Ozmutlu, S. (2014). Character n-gram application for automatic new topic identification. Information Processing & Management, 50(6), 821–856. https://doi.org/10.1016/j.ipm.2014.06.005
  22. Schmidt, C. W. (2019). Improving a tf-idf weighted document vector embedding. Retrieved from http://arxiv.org/abs/1902.09875
  23. Ren, J. (2012). ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging. Knowledge-Based Systems, 26, 144–153. https://doi.org/10.1016/j.knosys.2011.07.016
  24. Appel, O., Chiclana, F., Carter, J., & Fujita, H. (2016). A hybrid approach to the sentiment analysis problem at the sentence level. Knowledge-Based Systems, 108, 110–124. https://doi.org/10.1016/j.knosys.2016.05.040
  25. Tripathy, A., Agrawal, A., & Rath, S. K. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57, 117–126. https://doi.org/10.1016/j.eswa.2016.03.028
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