Abstract
Conversational sentiment analysis is an emerging,
yet challenging Artificial Intelligence (AI) subtask.
It aims to discover the affective state of each participant in a conversation. There exists a wealth of
interaction information that affects the sentiment of
speakers. However, the existing sentiment analysis approaches are insufficient in dealing with this
task due to ignoring the interactions and dependency relationships between utterances. In this paper, we aim to address this issue by modeling intrautterance and inter-utterance interaction dynamics.
We propose an approach called quantum-inspired
interactive networks (QIN), which leverages the
mathematical formalism of quantum theory (QT)
and the long short term memory (LSTM) network,
to learn such interaction dynamics. Specifically, a
density matrix based convolutional neural network
(DM-CNN) is proposed to capture the interactions
within each utterance (i.e., the correlations between
words), and a strong-weak influence model inspired
by quantum measurement theory is developed to
learn the interactions between adjacent utterances
(i.e., how one speaker influences another). Extensive experiments are conducted on the MELD
and IEMOCAP datasets. The experimental results
demonstrate the effectiveness of the QIN model.