Detecting affective characteristics of text with gated convolutional encoder-decoder framework

A computing system comprising:

• means for receiving, at a supervised classification engine, extracted linguistic features of an input text and a latent representation of the input text;
• means for predicting, by the supervised classification engine, an affect characterization of the input text using the extracted linguistic features and the latent representation, wherein predicting the affect characterization comprises:
• normalizing and concatenating a linguistic feature representation generated from the extracted linguistic features with the latent representation to generate an appended latent representation;
• and
• means for identifying, by a gated convolutional encoder-decoder model, an affect label of the input text using the predicted affect characterization.

A non-transitory computer-readable medium having instructions stored thereon, the instructions executable by a processing device to perform operations comprising:

• receiving, at a supervised classification engine, extracted linguistic features of an input text and a latent representation of the input text;
• predicting, by the supervised classification engine, an affect characterization of the input text using the extracted linguistic features and the latent representation, wherein predicting the affect characterization comprises:
• normalizing and concatenating a linguistic feature representation generated from the extracted linguistic features with the latent representation to generate an appended latent representation;
• and
• identifying, by a gated convolutional encoder-decoder model, an affect label of the input text using the predicted affect characterization, wherein the gated convolutional encoder-decoder model is jointly trained using a weighted auto-encoder loss associated with a reconstruction engine and a weighted classification loss associated with the supervised classification engine.