Mining Social sentiments for demand analysis in footwear industry

Abstract

The public tends to express their thoughts about particular goods and or services through popular social media networks such as Twitter and Facebook, while the firms also use social media to communicate with their consumers. As a result, this beneficial information can be used to make marketing and business decisions. However, due to the vast, noisy, and dynamic nature of these information, capturing the true public opinion has become a key challenge. Sentiment analysis is one of the methods that is employed to extract positive or negative attitudes from this social media information and thus, it has drawn the attention of the scholars during last decade. Different scholars have used a variety of techniques and methods to capture accurate results. Data mining is one of the recent approaches adopted by them to obtain better results from sentiment data. Moreover, some scholars have extended their studies to gain more insights from different topics. Such studies conducted to predict future results, analyze trends, detect anomalies etc. It can be beneficial for massive industries like footwear to understand their market through these approaches and streamline their product and service catalogs to meet the needs of their customers. This research aims to analyze previous studies conducted on this area, identify their contribution, challenges and limitations, and build a new comprehensive demand prediction model for footwear industry using data mining techniques.