Characterizing Social Relations Via NLP-Based Sentiment Analysis
Georg Groh, Jan Hauffa
2021Proceedings of the International AAAI Conference on Web and Social Media76 citationsDOIOpen Access PDF
Abstract
We investigate and evaluate methods for the characterization of social relations from textual communication context, using e-mail as an example. Social relations are intrinsically characterized by the Cartesian product of weights on various axes (we employ valuation and intensity as examples). The prediction of these characteristics is performed by application of unsupervised learning algorithms on meta-data, communication statistics, and the results of deep linguistic analysis of the message body. Classification of sentiment polarity is chosen as the means of linguistic analysis. We find that prediction accuracy can be improved by introducing limited amounts of additional information.
Topics & Concepts
Sentiment analysisComputer scienceArtificial intelligenceNatural language processingValuation (finance)Context (archaeology)Cartesian productPolarity (international relations)MathematicsGeographyBiologyGeneticsDiscrete mathematicsArchaeologyFinanceEconomicsCellAdvanced Text Analysis TechniquesSentiment Analysis and Opinion MiningComplex Network Analysis Techniques