  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.
