Original scientific paper

https://doi.org/10.17559/TV-20251124003154

A Computational Framework for Cross-Temporal Sentiment and Topic Analysis of Reddit Data

Yun Lai ; Northwest Normal University, 967 Anning E Rd, Anning District, Lanzhou, China, 730070
Zixiang Fan ; Beijing Union University, China 97 N 4th Ring E Rd, Chaoyang, Beijing, China, 100101 *

* Corresponding author.

Abstract

This paper develops and validates a modular computational framework for longitudinal sentiment and thematic analysis of unstructured social media data. While computational social science increasingly relies on automated tools, a gap remains in validated pipelines that rigorously integrate temporal dynamics with reliable sentiment metrics for noisy text. The proposed technical contribution is a three-stage pipeline comprising: (1) a comparative validation protocol establishing the superiority of lexicon-based rules (VADER) over un-tuned transformers (BERT) for specific informal domains; (2) a hybrid hierarchical topic modeling approach; and (3) a robust regression architecture cross-validated with LightGBM machine learning models. The framework's engineering utility is demonstrated through a 15-year dataset (N = 2267) of cross-cultural Reddit discourse. Results confirm the pipeline's ability to reliably detect event-driven signal shifts and isolate feature importance. This study offers engineers and data scientists a validated, replicable solution for high-noise public opinion mining and crisis monitoring applications.

Keywords

computational social science, natural language processing, Reddit; sentiment analysis; temporal analysis; topic modeling

Hrčak ID:

350442

URI

https://hrcak.srce.hr/350442

Publication date:

31.8.2026.

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