Affective Content Classification using Convolutional Neural Networks

Abstract

We present a three-layer convolutional neural network for the classification of two binary target variables 'Social' and 'Agency' in the HappyDB corpus exploiting lexical density of a closed domain and a high degree of regularity in linguistic patterns. Incorporating demographic information is demonstrated to improve classification accuracy. Custom embeddings learned from additional unlabeled data perform competitive to established pre-trained models based on much more comprehensive general training corpora. The top-performing model achieves accuracies of 0.90 for the 'Social' and 0.875 for the 'Agency' variable

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Fraunhofer-ePrints

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oai:fraunhofer.de:N-540840Last time updated on 4/30/2019

This paper was published in Fraunhofer-ePrints.

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