AUT Journal of Mathematics and Computing

AUT Journal of Mathematics and Computing

Analyzing Persian Twitter sentiments on the Arbaeen walk: A comparative study of LDA and BERTopic with the Arbaeen tweets dataset

Articles in Press

Document Type : Special Issue: MLKD 2024

Author
Zeynab Didehkhani
Independent Researcher, Tehran, Iran
10.22060/ajmc.2025.23870.1316
Abstract
As the world becomes increasingly interconnected, social media platforms like Twitter play a pivotal role in shaping public discourse. One significant topic that has emerged in recent years is the Arbaeen walk, a religious and political movement that has attracted global attention. This paper has two main objectives: first, to construct the Arbaeen Tweets Dataset, curated specifically for sentiment analysis; and second, to analyze Persian tweets related to the Arbaeen walk from 2021 to 022. By employing natural language processing (NLP) techniques—namely Latent Dirichlet Allocation (LDA) and BERTopic—we aim to uncover prevalent themes and sentiments, referred to as “topics” in topic modeling. The Arbaeen Tweets Dataset consists of 2,622 colloquial Persian texts from Twitter, labeled for sentiment analysis. Our findings indicate that while LDA demonstrates slightly superior quantitative performance, BERTopic offers greater coherence in topic interpretation.
Keywords
Text mining
Topic modeling
LDA
BERTopic
Dataset construction
Sentiment analysis
Arbaeen
Foot-pilgrimage
Twitter
Subjects
AUT Journal of Mathematics and Computing

Articles in Press, Accepted Manuscript
Available Online from 06 September 2026