Uncovering emotional and network dynamics in the speech of patients with chronic low back pain

BACKGROUND: Computational linguistics allows an understanding of language structure and different forms of expression of patients' perceptions. AIMS: The aims of this study were (i) to carry out a descriptive analysis of the discourse of people with chronic low back pain using sentiment analysis (SA) and network analysis; (ii) to verify the correlation between patients' profiles, pain intensity and disability levels with SA and network analysis; and (iii) to identify clusters in our sample according to language and SA using an unsupervised machine learning technique. METHODS: We performed a secondary analysis of a qualitative study including participants with chronic non-specific low back pain. We used the data related to participants' feelings when they received the diagnosis. The SA and network analysis were performed using the Valence Aware Dictionary and sEntiment Reasoner, and the Speech Graph, respectively. Clustering was performed using the K-means algorithm. RESULTS: In the SA, the mean composite score was -0.31 (Sd. = 0.58). Most participants presented a negative discourse (n = 41; 72%). Word Count (WC) and Largest Strongly connected Component (LSC) positively correlated with education. No statistically significant correlations were observed between pain intensity, disability levels, SA, and network analysis. Two clusters were identified in our sample. CONCLUSION: The SA showed that participants reported their feeling when describing the moment of the diagnosis using sentences with negative discourse. We did not find a statistically significant correlation between pain intensity, disability levels, SA, and network analysis. Education level presented positive correlation with WC and LSC.