Improving interpretations of topic modeling in microblogs

Abstract

Topic models were proposed to detect the underlying semantic structure of large collections of text documents to facilitate the process of browsing and accessing documents with similar ideas and topics. Applying topic models to short text documents to extract meaningful topics is challenging. The problem becomes even more complicated when dealing with short and noisy micro‐posts in Twitter that are about one general topic. In such a case, the goal of applying topic models is to extract subtopics. This results in topics represented by similar sets of keywords, which in turn makes the process of topic interpretation more confusing. In this paper we propose a new method that incorporates Twitter‐LDA, WordNet, and hashtags to enhance the keyword labels that represent each topic. We emphasize the importance of different keywords to different topics based on the semantic relationships and the co‐occurrences of keywords in hashtags. We also propose a method to find the best number of topics to represent the text document collection. Experiments on two real‐life Twitter datasets on fashion suggest that our method performs better than the original Twitter‐LDA in terms of perplexity, topic coherence, and the quality of keywords for topic labeling.

Twitter and Research: A Systematic Literature Review Through Text Mining

“…Topic Modeling: This method discloses the hidden semantic structure of tweets. The studies in this theme have proposed customized topic models for Twitter data or utilized already developed topic models such as incorporating Twitter-LDA, WordNet, and hashtags to enhance the quality of topic-discovery [81], developing a topic model based on high utility pattern mining to detect emerging topics [82], utilizing an already developed method, called hierarchical Dirichlet processes (HDP), to detect all posts related a given event [83], proposing a model based on Latent Dirichlet Allocation (LDA) to extract key phrases for social media events [84], and integrating the recurrent Chinese restaurant process and word co-occurrence analysis to propose a nonparametric topic model for short text documents such as tweets [85]. It is worth mentioning that the studies that utilized preexisting topic models used a qualitative approach for coding topics.…”

Managing buyer experience in a buyer–supplier relationship in MSMEs and SMEs

“…For the validity, we incorporate a two-stage process, in which first we include LDA, which provides topics and their associated words. These words are then mapped with the help of WordNet, a lexical database of English (Fellbaum, 2005 ), for a better representation of each topic, leading to a valid representation of topics and their associated words (Alkhodair et al, 2018 ). As for the reliability, we employ inter-coder reliability, in which one author has assigned each topic to its closely associated constructs, followed by other authors who mark the given relations.…”

Enabling Risk Mitigation for Pricing Strategies of Solar Energy

“…LDA algoritem je bil uspešno uporabljen na različnih področjih tudi pri analizi krajših besedilnih dokumentov kot so objave na spletnih forumih in socialnih (Alkhodair, Fung, Rahman in Hung, 2018;Rashad, Mohammed, El-Midany, Kandil in Ibrahim, 2007). Algoritem je implementiran v knjižnico lda programa R (Chang, 2015).…”