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“Computational Models For Multidimensional Annotations of Affect ”
by Anil Ramakrishna
December 2019
Affect is an integral aspect of human psychology which regulates all our interactions with external stimuli. It is highly subjective, with different stimuli leading to different affective responses in people due to varying personal and cultural artifacts. Computational modeling of affect is an important problem in Artificial Intelligence, which often involves supervised training of models using a large number of labeled data points. However, training labels are difficult to obtain due to the inherent subjectivity of affective dimensions. The most common approach to obtain the training labels is to collect subjective opinions or ratings from expert or naive annotators, followed by a suitable aggregation of the ratings.
In this dissertation, we will present our contributions towards building computational models for aggregating the subjective ratings of affect, specifically in the multidimensional setting. We propose latent variable models to capture annotator behaviors using additive Gaussian noise and matrix factorization models, which show improved performance in estimating the dimensions of interest. We then apply our matrix factorization model to the task of sentence level estimation of psycholinguistic normatives. Finally, we set up future work in estimating agreement on multidimensional annotations.