Recommendation System for Electronic Product
DOI:
https://doi.org/10.26438/ijcse/v7i7.16Keywords:
Recommendation System, Sentiment Analysis, Collaborative Filtering, PySparkAbstract
Recommendation System(RS) is one of machine that uses in many fields of application like music, book, shopping, and etc. With an RS, it makes users easier to find items that are very likely to be searched for. Not only star rating, but testimonials are also one of the data that affects buyers or connoisseurs of a product. The challenge is testimonial is not in numerical data type such as star rating. In this study, the researchers tried to build an architecture to combine the results of the testimonial through sentiment analysis and star rating which are processed separately in an RS. The dataset is reviews of few items in Amazon. The sentiment analysis uses Lexicon-based Approach, which RE use Collaborative filtering with PySpark library. The sentiment analysis has positive, negative, stop words, product-does corpora with double negative or positive words handling, cross negative-positive corpus words handling, and negative of product workless handling. The result is the architecture can be implemented with the testimonial and star rating dataset with giving recommendation items for every user.
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