Abstract
The data of reviews and ratings in the online market can provide guidance for company’s production and business activities. In this paper, firstly, we build a BP neural network model to help identify “useful consumer reviews.” Then, we use the fuzzy comprehensive evaluation method to identify the most successful and failing goods. Next, we achieve the time series prediction of product reputation by making use of ARIMA model. Finally, we use word segmentation and K-means clustering algorithm to determine whether stars and comments have radiation effects.
Highlights
Reviews system refers to the information feedback of consumers by rating and scoring the purchased goods
It can be seen that a large amount of online consumer generated data has gradually become an important tool for producers to identify consumer demand and make sales and production decisions [3]
It is of great significance to identify the new demand created by consumers due to the fact that the earlier the manufacturer discovers the consumer demand and enters the market, the greater and more lasting market share benefit can be obtained
Summary
Reviews system refers to the information feedback of consumers by rating and scoring the purchased goods. Is paper is based on the star rating and text evaluation data of three products (hair dryer, microwave oven, and baby pacifier) of Sunshine Company. We first establish a BP neural network prediction model to help identify effective reviews. By using this model, the company can obtain useful information on the rating and comment data of any goods on sale. We draw the conclusions, hoping that our results can help companies identify potential consumer demand and make production and sales decisions. Finding the indicators of data: After the data processing above, we get 5 data indicators: star_rating, vine, verify_purchase, review_length sum, and modified helpful_votes. We only analyze the situation of one product and do not process the rest again
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