Author Clustering and Topic Estimation for Short Texts

06/15/2021
by   Graham Tierney, et al.
0

Analysis of short text, such as social media posts, is extremely difficult because it relies on observing many document-level word co-occurrence pairs. Beyond topic distributions, a common downstream task of the modeling is grouping the authors of these documents for subsequent analyses. Traditional models estimate the document groupings and identify user clusters with an independent procedure. We propose a novel model that expands on the Latent Dirichlet Allocation by modeling strong dependence among the words in the same document, with user-level topic distributions. We also simultaneously cluster users, removing the need for post-hoc cluster estimation and improving topic estimation by shrinking noisy user-level topic distributions towards typical values. Our method performs as well as – or better – than traditional approaches to problems arising in short text, and we demonstrate its usefulness on a dataset of tweets from United States Senators, recovering both meaningful topics and clusters that reflect partisan ideology.

READ FULL TEXT

page 10

page 16

research
07/02/2020

A Novel Graph Based Clustering Approach to Document Topic Modeling

Clustering is the task of assigning a set of objects into groups so that...
research
12/20/2021

Improved Topic modeling in Twitter through Community Pooling

Social networks play a fundamental role in propagation of information an...
research
11/30/2020

A Framework for Authorial Clustering of Shorter Texts in Latent Semantic Spaces

Authorial clustering involves the grouping of documents written by the s...
research
03/17/2022

Short Text Topic Modeling: Application to tweets about Bitcoin

Understanding the semantic of a collection of texts is a challenging tas...
research
03/26/2020

Bag of biterms modeling for short texts

Analyzing texts from social media encounters many challenges due to thei...
research
08/03/2020

Deep Learning based Topic Analysis on Financial Emerging Event Tweets

Financial analyses of stock markets rely heavily on quantitative approac...
research
10/22/2018

Sparsemax and Relaxed Wasserstein for Topic Sparsity

Topic sparsity refers to the observation that individual documents usual...

Please sign up or login with your details

Forgot password? Click here to reset