Open Access

ARTICLE

Topic Modelling and Sentiment Analysis on YouTube Sustainable Fashion Comments

Hsu-Hua Lee, Minh T. N. Nguyen^*

Department of Management Sciences, College of Business and Management, Tamkang University, New Taipei City, 251201, Taiwan

* Corresponding Author: Minh T. N. Nguyen. Email:

Journal of New Media 2023, 5(1), 65-80. https://doi.org/10.32604/jnm.2023.045792

Received 07 September 2023; Accepted 17 November 2023; Issue published 27 December 2023

Abstract

YouTube videos on sustainable fashion enable the public to gain basic knowledge about this concept. In this paper, we analyse user comments on YouTube videos that contain sustainable fashion content. The paper’s main objective is to help content creators and business managers effectively understand the perspectives of viewers, thus improving video quality and developing business. We analysed a dataset of 17,357 comments collected from 15 sustainable fashion YouTube videos. First, we use Latent Dirichlet Allocation (LDA), a topic modelling technique, to discover the abstract topics. In addition, we use two approaches to rank these topics: ranking based on proportion and Rank-1 method. Second, we apply sentiment analysis to identify the user’s emotional tone in the comments. As a result, 14 topics were identified. The most common positive and negative scores are 1 and −1, respectively. In total, there are 28.42% positive comments, 22.35% negative comments and 49.23% neutral comments.

---

Keywords

---