Panpan Wang

TL;DR: A Quantum-inspired Multimodal Sentiment Analysis (QMSA) framework that aims to fill the “semantic gap” and model the correlations between different modalities via density matrix and significantly outperforms a wide range of baselines and state-of-the-art methods.

TL;DR: A novel and comprehensive framework for multimodal sentiment analysis in conversations is proposed, called a quantum-like multi-modal network (QMN), which leverages the mathematical formalism of quantum theory (QT) and a long short-term memory (LSTM) network.

TL;DR: 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.

TL;DR: A new conversational dataset is presented, named ScenarioSA, and an interactive long short-term memory network is proposed for conversational sentiment analysis to model interactions between speakers in a conversation, which outperforms a wide range of strong baselines and achieves competitive results with the state-of-art approaches.

TL;DR: The experimental results show that the model significantly outperforms a number of state-of-the-art baselines and demonstrate the effectiveness of the QSR model for sentiment analysis.