Have a personal or library account? Click to login

An evaluation of machine learning and latent semantic analysis in text sentiment classification

Open Access
|Oct 2020

References

  1. Agarwal, B., & Mittal, N. (2016). Machine Learning Approach for Sentiment Analysis. In Prominent Feature Extraction for Sentiment Analysis (pp. 21–45). Springer, Cham.10.1007/978-3-319-25343-5_3
  2. Andrew L. Maas, R. E. (2011). Learning Word Vectors for Sentiment Analysis. 49th annual meeting of the association for computational linguistics: Human language technologies, 142–150.
  3. Borg, I., & Groenen, P. J. (2005). Modern multidimensional scaling: Theory and applications. Springer Science & Business Media.
  4. Burrell, J. (2016). How the machine ‘thinks’: Understanding. Big Data & Society, 1–12. http://doi.org/10.1177/205395171562251210.1177/2053951715622512
  5. Cox, M. A., & Cox, T. F. (2008). Multidimensional scaling. In Handbook of data visualization. In Handbook of Data Visualization (pp. 315–347). Berlin, Heidelberg: Springer.
  6. D. Tang, F. W. (2014). Learning Sentiment-Specific Word Embedding. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 1, 1555–1565.
  7. Dos Santos, C. N., & Gatti, M. (2014). Deep Convolutional Neural Networks for. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, 69–78.
  8. Jifara, W., Jiang, F., Rho, S., Cheng, M., & Liu, S. (2019). Medical image denoising using convolutional neural network: a residual learning approach. The Journal of Supercomputing, 704–718.10.1007/s11227-017-2080-0
  9. Krouska, A., Troussas, C., & Virvou, M. (2016). The effect of preprocessing techniques on Twitter sentiment analysis. 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA), IEEE, 1–6.10.1109/IISA.2016.7785373
  10. Kruskal, J. B. (1964). Nonmetric multidimensional scaling: A numerical approach. Psychometrika.10.1007/BF02289694
  11. Kruskal, J. B. (1978). Multidimensional scaling. Sage.10.4135/9781412985130
  12. Mattila, M., & Salman, H. (2018). Analysing Social Media Marketing on Twitter using Sentiment Analysis. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229787 (access: 20/06/2020).
  13. Miazga, J., & Hachaj, T. (n.d.). Datasets and source code used in this article. Retrieved from https://github.com/JusMia/sentimentanalysis_ML (August 20, 2020).
  14. Ramteke, J., Shah, S., Godhia, D., & Shaikh, A. (2016). Election result prediction using Twitter sentiment analysis. 2016 international conference on inventive computation technologies (ICICT), Vol. 1, IEEE, 1–5.10.1109/INVENTIVE.2016.7823280
  15. Salminen, J., Yoganathan, V., Corporan, J., Jansen, B. J., & Jung, S.-G. (2019). Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type. Journal of Business Research, 203–217.10.1016/j.jbusres.2019.04.018
  16. Santra, A. K. (2012). Genetic Algorithm and Confusion Matrix for Document Clustering. International Journal of Computer Science Issues (IJCSI), 9(1), 322–328.
  17. Sebastiani, F. (2002). Consiglio Nazionale Delle Ricerche. Machine learning in automated text categorization. ACM Computing Surveys, 34, 1–47.10.1145/505282.505283
  18. Shimodaira, H., Noma, K.-I., Nakai, M., & Sagayama, S. (2002). Dynamic Time-Alignment Kernel in Support Vector Machine. Advances in neural information processing systems, 21–928.
  19. Soucy, P. &. (2005, July). Beyond TFIDF weighting for text categorization in the vector space model. IJCAI, 5, 1130–1135.
  20. Tripathy, A., Agrawal, A., & Rath, S. K. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57, 117–126.10.1016/j.eswa.2016.03.028
  21. Trsteniak, B., Mikac, S., & Donko, D. (2014). KNN with TF-IDF based Framework for Text Categorization. Procedia Engineering, 69, 1356–1364.10.1016/j.proeng.2014.03.129
  22. Wang, X., Zhang, C., Ji, Y., Sun, L., Wu, L., & Bao, Z. (2013). A depression detection model based on sentiment analysis in micro-blog social network. Pacific- -Asia Conference on Knowledge Discovery and Data Mining, 201–213.10.1007/978-3-642-40319-4_18
  23. Yan, B. Y. (2017). Microblog sentiment classification using parallel SVM in apache spark. 2017 IEEE International Congress on Big Data (BigData Congress), IEEE, 282–288.10.1109/BigDataCongress.2017.43
DOI: https://doi.org/10.37705/TechTrans/e2020030 | Journal eISSN: 2353-737X | Journal ISSN: 0011-4561
Language: English
Submitted on: Jun 20, 2020
Accepted on: Sep 22, 2020
Published on: Oct 1, 2020
Published by: Cracow University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2020 Justyna Miazga, Tomasz Hachaj, published by Cracow University of Technology
This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 License.