NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity

2017-09-01 · WS 2017 9 · Vladimir Andryushechkin, Ian Wood, James O{'} Neill

This paper describes the entry NUIG in the WASSA 2017 (8th Workshop on Computational Approaches to Subjectivity, Sentiment {\&} Social Media Analysis) shared task on emotion recognition. The NUIG system used an SVR (SVM regression) and BLSTM ensemble, utilizing primarily n-grams (for SVR features) and tweet word embeddings (for BLSTM features). Experiments were carried out on several other candidate features, some of which were added to the SVR model. Parameter selection for the SVR model was run as a grid search whilst parameters for the BLSTM model were selected through a non-exhaustive ad-hoc search.

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Tasks

Emotion Recognition regression Speech RecognitionWord Embeddings

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