Longitudinal Sentiment Analysis with Conversation Textual Data  

作　　者：Haiyan Liu Shelly Tsang Adrienne Wood Xin Tong 

机构地区：[1]Department of Psychological Sciences,University of California Merced,Merced,USA [2]Department of Psychology,University of Virginia,216D Gilmer Hall,Charlottesville,USA

出　　处：《Fudan Journal of the Humanities and Social Sciences》2025年第1期193-214,共22页复旦人文社会科学论丛(英文版)

基　　金：supported by the National Science Foundation under Grant No.:SES-1951038.

摘　　要：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.

关 键 词：Text mining Sentiment analysis Longitudinal textual data Growth curve modeling Dialogue data 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]