Semantic Properties of Customer Sentiment in Tweets

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.Comment: The 28th IEEE International Conference on Advanced Information Networking and Applications. Victoria, Canada, 201

Analyzing and predicting Lifetime of trends using social networks

“…In [1], Eun Hee Ko and Diego Klabjan used text mining to find semantic patterns in the discussions on different US Retail Companies on a social networking site. Alexander Pak and Patrick Paroubek, in [2], automatically collect a corpus on Twitter and then go on to analyze the corpus and build a sentiment analyzer that is able to determine positive, negative and neutral sentiments in a document.…”

Rapid Text Retrieval and Analysis Supporting Latent Dirichlet Allocation Based on Probabilistic Models

“…ere is a need to identify the conversations that take place in the community such as on YouTube, Facebook, blog posts, and tweets, where the comments should be in the public mind. Text Analytics is a promising solution and is useful in these times [1,2].…”

“…Text analysis applies to categorizing or mining information from the text about the environment [2]. e purpose of text analysis is to create a semantic structure based on differences in the concept, meaning, and attitude of the context.…”