Semantic Properties of Customer Sentiment in Tweets

TL;DR

This paper explores the semantic patterns of consumer sentiment in tweets about retail companies using text mining techniques, revealing semantic trends and topics beyond basic sentiment analysis.

Contribution

It introduces a novel analysis of semantic properties in consumer tweets, employing clustering and topic modeling to uncover deeper insights into sentiment and opinions.

Findings

Identified semantic similarities and dissimilarities in consumer opinions.

Discovered latent topics representing positive and negative sentiments.

Demonstrated the effectiveness of LDA in analyzing social media sentiment data.

Abstract

An increasing number of people are using online social networking services (SNSs), and a significant amount of information related to experiences in consumption is shared in this new media form. Text mining is an emerging technique for mining useful information from the web. We aim at discovering in particular tweets semantic patterns in consumers' discussions on social media. Specifically, the purposes of this study are twofold: 1) finding similarity and dissimilarity between two sets of textual documents that include consumers' sentiment polarities, two forms of positive vs. negative opinions and 2) driving actual content from the textual data that has a semantic trend. The considered tweets include consumers opinions on US retail companies (e.g., Amazon, Walmart). Cosine similarity and K-means clustering methods are used to achieve the former goal, and Latent Dirichlet Allocation (LDA), a popular topic modeling algorithm, is used for the latter purpose. This is the first study which discover semantic properties of textual data in consumption context beyond sentiment analysis. In addition to major findings, we apply LDA (Latent Dirichlet Allocations) to the same data and drew latent topics that represent consumers' positive opinions and negative opinions on social media.