Weighted high-order hidden Markov models for compound emotions recognition in text

Emotion recognition in text has attracted a great deal of attention recently due to many practical applications and challenging research problems. In this paper, we explore an efficient identification of compound emotions in sentences using hidden Markov models (HMMs). In this problem, emotion has temporal structure and can be encoded as a sequence of spectral vectors spanning an article range. The major contributions of the research include the (i) proposal of weighted high-order HMMs to determine the most likely sequence of sentence emotions in an article. The weighted high-order HMMs take into account the impact degree of context emotions with different lengths of history; (ii) introduction of a representation of compound emotions by a sequence of binary digits, namely emotion code; (iii) development of an architecture that uses the emotions of simple sentences as part of known states in the weighted high-order hidden Markov emotion models for further recognizing more unknown sentence emotions. The experimental results show that the proposed weighted high-order HMMs is quite powerful in identifying sentence emotions compared with several state-of-the-art machine learning algorithms and the standard n-order hidden Markov emotion models. And the use of emotion of simple sentences as part of known states is able to improve the performance of the weighted n-order hidden Markov emotion models significantly.