Product Feature Ranking and Popularity Model based on Sentiment Comments

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.

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