Abstract: This paper proposes the development of a model to determine feature popularity ranking for products in the market. Each feature that is reviewed by a customer has a relation to sentiment words present in the sentences within a customer review. Feature quantity of a product, derived from customer review dataset, cannot be used as a benchmark to determine customers’ preferences since each feature is influenced by sentiment words that give it either a positive or negative meaning. A positive meaning shows that the feature is liked by user; and a negative meaning shows that it is disliked by user. This study finds that sentiment assessments by users play an important role in determining feature popularity ranking; and they affect the feature of a product. Thus, this study proposes the development of a model that takes into account the importance of sentiment assessments present in each sentence within a customer review of a product feature. A case study has been conducted in proving that the developed model is able to produce a list of product feature popularity ranking. Results of this experimental model is also put into simple comparative analysis with a few models from previous studies.

Siti Rohaidah Ahmad, Azuraliza Abu Bakar, Mohd Ridzwan Yaakub, Nurhafizah Moziyana Mohd Yusop, Muslihah Wook and Arniyati Ahmad, “Product Feature Ranking and Popularity Model based on Sentiment Comments” International Journal of Advanced Computer Science and Applications(IJACSA), 9(9), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090921

@article{Ahmad2018,
title = {Product Feature Ranking and Popularity Model based on Sentiment Comments},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2018.090921},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090921},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {9},
author = {Siti Rohaidah Ahmad and Azuraliza Abu Bakar and Mohd Ridzwan Yaakub and Nurhafizah Moziyana Mohd Yusop and Muslihah Wook and Arniyati Ahmad},
}