Abstract
Emotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018). Previous studies handle emotion lexicon construction and emotion classification separately. In this paper, we propose an emotional network (EmNet) to jointly learn sentence emotions and construct emotion lexicons which are dynamically adapted to a given context. The dynamic emotion lexicons are useful for handling words with multiple emotions based on different context, which can effectively improve the classification accuracy. We validate the approach on two representative architectures – LSTM and BERT, demonstrating its superiority on identifying emotions in Tweets. Our model outperforms several approaches proposed in previous studies and achieves new state-of-the-art on the benchmark Twitter dataset.
Highlights
The last several years have seen a land rush in TweetEmotion research on identification of emotions in short text such as Twitter or product reviews due to its greatly commercial value
We propose a novel emotional network (EmNet), which consists of three main components: 1. Sentence encoder encodes the input sentence into semantic hidden states, which can be implemented as either LSTM or BERT
We propose a novel emotional network for multi-label emotion classification which jointly learns emotion lexicons and conducts classification
Summary
The last several years have seen a land rush in TweetEmotion research on identification of emotions in short text such as Twitter or product reviews due to its greatly commercial value. The emotions (e.g., anger or joy) expressed in prod-. This is a joke really how long will he keep diving and ducking. Disgust joy uct reviews can be a major factor in deciding the Table 1: Example sentences and their emotions. The SOTA approaches to this task (Baziotis et al, 2018; Meisheri and Dey, 2018) generally employ pre-defined emotion lexicons, which have two major limitations: 1. Most established emotion lexicons were created for a general domain, and suffer from limited coverage and inaccuracies when applied to the highly informal short text. 2. The pre-defined lexicons suffer from the ambiguity problem: the emotion of a word is highly influenced by the context.
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