Abstract:
This paper presents a method to correlate relevant product features to the
sales rank data. Instead of going through the labor-intensive surveys, online product
reviews have become an efficient source to gather consumer preferences. The
contribution of the paper is to relate the content of reviews to a product’s sales rank
that implicitly reflects the motivation behind what drives customers to purchase the
product. After using part-of-speech tagging to extract the relevant feature and
opinion pairs from the reviews, the extracted data along with the review ratings and
price become the variables to explain the sales rank. An experiment is run for
wearable technology products to illustrate the methodology and interpret the result.
The result indicates that the positive opinion for battery and negative opinion for
sleep tracker are significant towards sales rank, while price is not.
