Analysis of Topic and Sentiment Trends in Customer Reviews Before and After Covid-19 Pandemic

Abstract

The Covid-19 pandemic has impacted many people’s lives. Many researches have studied the impact of the pandemic on customer opinion change regarding services, yet there are still few researches regarding the change towards products. As a product category that experienced a significant increase in sales since the pandemic began, headphones have become a suitable product category to analyze the change. To analyze the change, this paper aims to discover the topics that customers discuss in their reviews. Latent Dirichlet Allocation (LDA) is selected as the topic modeling method to obtain the topics (i.e., aspects of a product) that are discussed in the customer reviews. In the case study, six topics that are discussed by customers are discovered, i.e., Durability Issues, Usage Contexts, Noise Cancellation, Features, Quality, and Customer Service. The monthly proportion of sentences that discuss a topic provides the topic trend. Among those six topics, the discussion about the Usage Contexts topic has increased since the beginning of the pandemic, while the other topics do not show a clear trend related to the pandemic. SentiWordNet is selected as the sentiment analysis method to capture the positive and negative sentiment towards the topics. Among the six topics, the Durability Issues and Noise Cancellation topics showed an improved sentiment after the pandemic began, while the sentiment for Usage Contexts, Features, and Quality topics worsened. Future research may be suggested to explain the worsening trend for those topics, especially the Usage Contexts topic that gained significant negativity after the pandemic began.