Lexuan Zhong, Feng-Chiao Su, Stuart Batterman
Volatile Organic Compounds (VOCs) in Conventional and High Performance School Buildings in the U.S
International journal of environmental research and public health
sentic computing relies welldefined affective models different complexitypolarity distinguish positive negative sentiment example nuanced models capture expressions human emotions used measure communication success even granular affective model combined sophisticated machine learning approaches may fully capture organisations strategic positioning goals goals often deviate assumptions standardised affective models certain emotions joy trust typically represent desirable brand associations specific communication goals formulated marketing professionals often go beyond standard dimensions instance brand manager television show may consider fear sadness desired emotions audience article introduces expansion techniques affective models combining common commonsense knowledge available knowledge graphs language models affective reasoning improving coverage consistency well supporting domainspecific interpretations emotions extensive evaluation compares performance different expansion techniques quantitative evaluation based revisited hourglass emotions model assess performance complex models cover multiple affective categories using manually compiled gold standard data ii qualitative evaluation domainspecific affective model television programme brands results evaluations demonstrate introduced techniques support variety embeddings pretrained models paper concludes discussion applying approach scenarios affective model resources scarce