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Longitudinal Sentiment Analysis with Conversation Textual Data

2024; Springer Science+Business Media; Volume: 18; Issue: 1 Linguagem: Inglês

10.1007/s40647-024-00417-0

1674-0750

Haiyan Liu, Shelly Tsang, Adrienne Wood, Xin Tong,

Topic Modeling

Abstract The inherent qualitative nature of textual data poses significant challenges for direct integration into statistical models. This paper presents a two-stage process for analyzing longitudinal textual data, offering a solution to this inherent challenge. The proposed model comprises (1) initial data preprocessing and sentiment extraction, followed by (2) applying a growth curve model to analyze the extracted sentiments directly. The paper also explores four distinct approaches for extracting sentiment scores in the dialogue, providing versatility to the proposed framework. The practical application of the proposed model is demonstrated through the analysis of an empirical longitudinal textual dataset. This framework offers a valuable contribution to the field by addressing the challenges associated with modeling qualitative textual data, providing a robust methodology for extracting and analyzing sentiments longitudinally.