Sentiment and Emotion Network Analysis—SENA: An Analytic Framework and No-Code Software to Integrate Natural Language Processing, Linguistic Analysis, and Data Science with Qualitative and Mixed-Methods Research
Manuel S. González Canché, Kaiwen Zheng · International Journal of Qualitative Methods · 2025
Sentiment and emotion analyses (SEA) provide us with the opportunity to gain objective and fast insights into the experiences of vast amounts of individuals. Despite SEA’s efficiency, two main limitations associated with its use remain: outcome aggregation and narrow applicability. Outcome aggregation means that outputs do not consider participants’ attributes nor allow for individual analyses of participants’ experiences and/or the degree to which these experiences may express consistent or contradictory emotions (i.e., emotional entropy). Narrow applicability means that SEA has primarily been employed in market research to understand customers’ satisfaction thus foregoing its applicability in qualitative and mixed methods academic research. With these opportunities and limitations in mind, our study was designed to offer an analytic framework that avoids outcome aggregation and expands the use of SEA beyond market research. As part of our study’s contribution, we offer a free to use and free to distribute software tool that implements the totality of our proposed analytic framework. This software does not require any programming proficiency and executes all required back-end processes locally—thus avoiding the need to upload our data to any servers. In our proposed framework, outcome aggregation is avoided via network modeling that also enables testing for potential differences in emotions distributions across groups. Based on this integration, we refer to the resulting analytic framework and software as Sentiment and Emotion Network Analysis—SENA. As part of our methodological and research design discussion, we also illustrate how to integrate SENA with machine learning text classification of documents or open-ended responses. This integration serves to leverage the power of artificial intelligence in studies relying on SENA. The databases used to showcase this analytic framework are provided so that researchers may interact firsthand with the SENA framework and its externally peer-reviewed software ( https://cutt.ly/BrhEnyfY ) available in Mac ( https://cutt.ly/QwhYruBr ) and Windows ( https://cutt.ly/YwhJJKvO ) operating systems.