Opinion Mining Terhadap Toko Online Di Media Sosial Menggunakan Algoritma Naïve Bayes (Studi Kasus: Akun Facebook Dugal Delivry)

Yustia Hapsari, Muhammad Fikri Hidayattullah, Dairoh Dairoh, Mohammad Khambali

Abstract


The Internet era has had an impact in various sectors of human life. One is the economic sector. Economic transactions change from the traditional pattern (face to face) to online. The customer does not need to ask about the condition of an item to be purchased to a close friend or family, but simply by reviewing the product from the same buyer's comments. Products that get good reviews mean good quality. However, a problem arises if the comment data is very large and will make it difficult for customers to summarize the quality. Therefore, an automatic opinion mining system is required which can directly give conclusions about the quality of a product. This research makes an opinion mining system by applying the Naïve Bayes algorithm by taking a case study of facebook account Dugal Delivry. The measurement result with confusion matrix gives precision value of 88,89%, recall 80% and accuracy equal to 85%.

Full Text:

References


R. Othman, R. Belkaroui, and R. Faiz, “Extracting Extracting Product Product Features Features for for Opinion Opinion Mining Mining Using Public Conversations in Twitter Using Public Conversations in Twitter,” Procedia Computer Science, vol. 112, pp. 927–935, 2017.

B. Liu, “Sentiment Analysis and Opinion Mining,” Synthesis Lectures on Human Language Technologies, no. May, 2012.

L. L. Dhande and P. G. K. Patnaik, “Analyzing Sentiment of Movie Review Data using Naive Bayes Neural Classifier,” International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), vol. 3, no. 4, pp. 313–320, 2014.

M. Ringsquandl and D. Petković, “Analyzing Political Sentiment on Twitter,” Analyzing Microtext: Papers from the 2013 AAAI Spring Symposium, pp. 40–47, 2013.

A. Bakliwal, J. Foster, J. Van Der Puil, R. O. Brien, L. Tounsi, and M. Hughes, “Sentiment Analysis of Political Tweets : Towards an Accurate Classifier,” Proceedings of the Workshop on Language in Social Media (LASM 2013), no. Lasm, pp. 49–58, 2013.

T. Ding, J. Deng, J. Li, and Y. Lin, “Sentiment Analysis and Political Party Classification in 2016 U . S . President Debates in Twitter,” International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation, pp. 1–6, 2016.

R. Malinský and I. Jelínek, “Sentiment Analysis : Popularity of Candidates for the President of the United States,” International Journal of Humanities and Social Sciences, vol. 6, no. 12, pp. 3679–3681, 2012.

E. Tunggawan, “And the Winner is ...: Bayesian Twitter-based Prediction on 2016 U . S . Presidential Election,” IC3INA 2016 Conference (3-5, no. 1, pp. 1–5, 2016.

A. Ortigosa, J. M. Martín, and R. M. Carro, “Computers in Human Behavior Sentiment analysis in Facebook and its application to e-learning,” COMPUTERS IN HUMAN BEHAVIOR, 2013.

L. Dey, S. Chakraborty, A. Biswas, B. Bose, and S. Tiwari, “Sentiment Analysis of Review Datasets using Naïve Bayes ’ and K-NN Classifier,” Information Retrieval (cs.IR); Computation and Language (cs.CL), vol. 8, no. 4, pp. 54–62, 2016.

S. Gusriani, K. D. K. Wardani, and M. I. Zul, “Analisis Sentimen Terhadap Toko Online di Sosial Media Menggunakan Metode Klasifikasi Naïve Bayes ( Studi Kasus ... Analisis Sentimen Terhadap Toko Online di Sosial Media,” 4th Applied Business and Engineering Conference, no. November, 2016.

B. Liu, E. Blasch, Y. Chen, D. Shen, E. Blasch, Y. Chen, D. Shen, and G. Chen, “Scalable sentiment classification for Big Data analysis using Naïve Bayes Classifier Scalable Sentiment Classification for Big Data Analysis Using Na ¨ ıve Bayes Classifier,” IEEE Intl Conf. on Big Data, no. October 2013, 2014.

T. R. Patil and S. S. Sherekar, “Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification,” International Journal Of Computer Science And Applications, vol. 6, no. 2, 2013.

I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Third Edition. Elsevier Inc., 2011.

F. Gorunescu, Data Mining: Concepts,Models and Techniques, 12th ed. Springer, 2011.




DOI: https://doi.org/10.30591/jpit.v3i2.919

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

JPIT INDEXED BY

  
  

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.