Reading reviews helps consumers choose the
applications, helping companies and developers monitor user satisfaction to
improve quality of features and services, read overall and manually could spend
the time and laborious, if read at a glance, information not conveyed
perfectly. This study analyzes user sentiment Windows Phone Store applications
by automatically classifying reviews into positive or negative opinion
category. Naïve Bayes has good potential because of its simplicity and
performance as a model of classifying text on many domains. The model was
evaluated using 10 Fold Cross Validation. Measurements were made with the
Confusion Matrix and the ROC curve. The accuracy produced in this study is
84.50%, indicating that Naïve Bayes is a good model in classifying text
especially in the case of sentiment analysis.
Feldman, R. (2013).
Techniques and applications for sentiment analysis. Communications of the
ACM, 56(4), 82–89.
Gorunescu, F. (2011). Data Mining: Concepts and Techniques.
Verlag berlin Heidelberg: Springer.
Ha, E., & Wagner, D. (2013). Do Android users write about
electric sheep? Examining consumer reviews in Google Play. 2013 IEEE 10th
Consumer Communications and Networking Conference, CCNC 2013, 149–157.
https://doi.org/10.1109/CCNC.2013.6488439
Harlian, M. (2006). Text Mining.
Moraes, R., Valiati, J. F., & Neto, W. P. G. (2013).
Document-level sentiment classification: An empirical comparison between SVM
and ANN. Expert Systems with Applications, 40(2), 621–633.
Retrieved from linkinghub.elsevier.com/retrieve/pii/S0957417412009153
Ting, S. L., Ip, W. H., & Tsang, A. H. C. (2011). Is
Naïve bayes a good classifier for document classification? International
Journal of Software Engineering and Its Applications, 5(3), 37–46.
Witten, I. H., & Frank, E. (2011). Data Mining
Practical Machine Learning Tools And Technique. Burlington: Elsevier Inc.
https://doi.org/0120884070, 9780120884070
Yang, C. C., Tang, X., Wong, Y. C., & Wei, C.-P. (2010).
(2010). Understanding Online Consumer Review Opinions with Sentiment Analysis
using Machine Learning. Journal of the Association for Information Systems,
2(3), 73–89. Retrieved from http://aisel.aisnet.org/pajais/vol2/iss3/7/
