skip to main content

Analisis Sentimen Berbasis Ontologi di Level Kalimat untuk Mengukur Persepsi Produk

*Agus Subhan Akbar  -  Magister Sistem Informasi Universitas Diponegoro, Indonesia
Eko Sediyono  -  Magister Sistem Informasi Universitas Kristen Satya Wacana, Indonesia
Oky Dwi Nurhayati  -  Jurusan Sistem Komputer, Fakultas Teknik, Universitas Diponegoro, Indonesia

Citation Format:

The purpose of this research is to do sentiment analysis on tweets data retrieved using ontology framework and using naïve bayes classifier algorithm for classification process. This study is based on the habits of twitter users who frequently writes opinion, expression, or sentiment on a specific product, especially smartphones. These tweets can be used as a basis for sentiment analysis on a particular product. The method used in this study include the use of ontology framework for tweets retrieval that match the domain of the discussion and the use of naïve bayes classification algorithm for data classification. Classification process carried past the 3 pieces of layer classification to fine tune the final result of classification. Three layers of classification used include buzz/promo classification (classifying tweets into buzz and not-buzz tweets), subjectivity classification (classifying not-buzz tweets into subjective and objective tweets), and sentiment classification (classifying subjective tweets into positive, negative, or neutral tweets). The resulted software can classify tweets with high accuracy. This software was trained and tested with the composition of 25:75, 50:50, 75:25 from sample data and tested 10 times for each composition. Average accuracy of the system reached 84.16%, 86.15%, and 87.54% for each composition. The result showed that by employing this system, product marketing stakeholders can determine the level of user sentiment expressed in the form of tweets. The method used in this study could be developed to improve the accuracy of classification systems.



Fulltext View|Download
Keywords: Sentence Level Sentiment Analysis; Ontology; Naïve Bayes, Classification

Article Metrics:

  1. Abrahamsson, P., Salo, O., Ronkainen, J. dan Warsta, J., 2002. Agile software development methods: Review and analysis, VTT Publication
  2. Chen, L., Wang, F., Qi, L., dan Liang, F., 2014. Experiment on sentiment embedded comparison interface, Knowledge-Based Systems, 64, 44-58
  3. Ghiassi, M., Skinner, J., dan Zimbra, D., 2013. Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network, Expert Systems with Applications: An International Journal, 40(16), 6266-6282
  4. Haddi, E., Liu, X., dan Shi, Y., 2013. The role of text pre-processing in sentiment analysis, Procedia Computer Science, 17, 26-32
  5. He, Y., dan Zhou, D., 2011. Self-training from labeled features for sentiment analysis, Information Processing & Management, 47 (4), 606-616
  6. Kontopoulos, E., Berberidis, C., Dergiades, T., dan Bassiliades, N., 2013. Ontology-based sentiment analysis of twitter posts, Expert systems with applications, 40(10), 4065-4074
  7. Liu, B., 2011. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Second Edition, Springer
  8. Liu, B., 2010. Sentimen Analysis and Subjectivity In: Indurkhya, N., Damerau, F. J. (Eds.), HANDBOOK OF NATURAL LANGUAGE PROCESSING, Second Edition. Boca Raton, FL, 627-666
  9. Maks, I., dan Vossen, P., 2012. A lexicon model for deep sentiment analysis and opinion mining applications, Decision Support Systems, 53(4), 680-688
  10. Manning, C.D., Raghavan, P., dan Schütze, H., 2008. Introduction to information retrieval, Cambridge: Cambridge university press
  11. Medhat, W., Hassan, A., dan Korashy, H., 2014. Sentiment analysis algorithms and applications: A survey,Ain Shams Engineering Journal,5, 1093-1113
  12. Mukherjee, S., dan Sharma, N., 2012. Intrusion detection using naive Bayes classifier with feature reduction, Procedia Technology, 4, 119-128
  13. Peñalver-Martinez, I., Garcia-Sanchez, F., Valencia-Garcia, R., Rodríguez-García, M. Á., Moreno, V., Fraga, A., dan Sánchez-Cervantes, J. L., 2014. Feature-based opinion mining through ontologies, Expert Systems with Applications, 41(13), 5995-6008
  14. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., dan Stede, M., 2011. Lexicon-based methods for sentiment analysis, Computational linguistics, 37(2), 267-307
  15. Toutanova, K., Klein, D., Manning, C. D., dan Singer, Y., 2003. Feature-rich part-of-speech tagging with a cyclic dependency network, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol 1, 173-180
  16. Unnamalai, K., 2012. Sentiment analysis of products using web, Procedia Engineering, 38, 2257-2262
  17. Wicaksono, A. F., dan Purwarianti, A., 2010. HMM based part-of-speech tagger for Bahasa Indonesia, In Fourth International MALINDO Workshop, Jakarta
  18. Zhang, H., 2004. The optimality of naive Bayes,Proceedings of the Seventeenth Florida Articial Intelligence Research Society Conference, Florida,562-567
  19. Zhang, L., Ghosh, R., Dekhil, M., Hsu, M., dan Liu, B., 2011. Combining lexicon-based and learning-based methods for Twitter sentiment analysis, HP Laboratories, Technical Report HPL-2011, 89
  20. Zhou, S., Chen, Q., dan Wang, X., 2014. Fuzzy deep belief networks for semi-supervised sentiment classification, Neurocomputing, 131, 312-322

Last update:

  1. Enhancing Sentiment Analysis Accuracy in Borobudur Temple Visitor Reviews through Semi-Supervised Learning and SMOTE Upsampling

    Candra Agustina, Purwanto Purwanto, Farikhin Farikhin. Journal of Advances in Information Technology, 15 (4), 2024. doi: 10.12720/jait.15.4.492-499

Last update: 2024-06-23 01:59:20

No citation recorded.