With the development of the e-commerce industry, various brands of products with different qualities and functions continuously emerge, and the number of online shopping users is increasing every year. After purchase, users always leave product comments on the platform, which can be used to help consumers choose commodities and help the e-commerce companies better understand the popularity of their goods. At present, the e-commerce platform lacks an effective way to measure customer satisfaction based on various customer comments features. In this paper, our goal is to build a product customer satisfaction measurement by analyzing the relationship between the important attributes of reviews and star ratings. We first use an improved information gain algorithm to analyze the historical reviews and star rating data to find out the most informative words that the purchasers care about. Then, we make hypotheses about the relevant factors of the usefulness of reviews and verify them using linear regression. We finally establish a customer satisfaction measurement based on different review features. We conduct our experiments based on three products with different brands chosen from the Amazon online store. Based on our experiments, we discover that features such as length and extremeness of the comments will affect the review usefulness, and the consumer satisfaction measurement constructed using the exponential moving average method can effectively reflect the trend of user satisfaction over time. Our work can help companies acquire valuable suggestions to improve product features, increase sales, and help customers make wise purchases.
Social influence analysis is a very popular research direction. This article analyzes the social network of musicians and the many influencing factors when musicians create music to rank the influence of musicians. In order to achieve the practical purpose of the model making accurate predictions in the broad music market, the algorithm adopts a macromodel and considers the social network topology network. The article adds the time decay function and the weight of genre influence to the traditional PageRank algorithm, and thus, the MRGT (Musician Ranking based on Genre and Time) algorithm appears. Considering the timeliness of social networks and the continuous development of music, we realized the importance of evolving MRGT into a dynamic social network. Therefore, we adopted audio data analysis technology and used Gaussian distance to classify and study the evolution of music properties at different times and different genres and finally formed the dynamic influence ranking algorithm based on musicians’ social and personal information networks. As a macromodel heuristic algorithm, our model is explanatory, can handle batch data and can avoid unfavorable factors, so as to provide fast speed and improved accuracy. The network can obtain an era indicator DMI (Dynamic Music Influence) that measures the degree of music revolution. DMI is the indicator we provide for music companies to invest in musicians.
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