This study proposes an ensemble model to incorporate sensory features
of lexical items in English from external resources into neural affective analysis
frameworks. This allows the models to take the combined effects of bi-directional
feeling between the sensory lexicon and the writer to infer human affective
knowledge. We evaluate our model on two affective analysis tasks. The ensemble
model exhibits the best accuracy and the results with 1% F1-score improvement
over the baseline LSTM model in the sentiment analysis task. The performance
shows that perceptual information can contribute to the performance of sentiment
classification tasks significantly. This study also provides a support for the
linguistic finding that correlations exist between sensory features and sentiments
in the language
