Text Mining for Ford Car Review Data – Based on Sentiment Analysis, LDA Topic Model, Semantic Web Analysis

Abstract

As an established automotive brand with its origins in the early twentieth century, the Ford brand has developed a distinctive global influence over the course of decades. In order to gain insight into customer perceptions of the brand, this study employs a data set comprising customer evaluations of the Ford automobile between 2002 and 2018. Firstly, word frequency statistics are calculated in order to demonstrate the key points that are predominantly mentioned by customers. Subsequently, a sentiment analysis is conducted to categorise the keywords into two distinct groups: the positive and the negative. These groups represent the positive and constructive feedback provided by customers, respectively. Subsequently, latent Dirichlet allocation based on sentiment analysis was employed to identify five themes comprising positive and negative words. These included car appearance, user experience, car performance, comfort, and brand recognition. Furthermore, in order to gain insight into the semantic relationships between these reviews, a semantic network analysis was employed to demonstrate the interactions between the key points. In conclusion, the above analysis provides an overview of the Ford brand based on user reviews. The findings of this study may prove valuable in informing future improvements to Ford's automobile sales and manufacturing processes.