Lu Rong

Bio: Lu Rong is an academic researcher from Zhengzhou University of Light Industry. The author has contributed to research in topics: Computer science & Sentiment analysis. The author has an hindex of 2, co-authored 2 publications receiving 21 citations.

ScenarioSA: A Dyadic Conversational Database for Interactive Sentiment Analysis

Abstract: Interactive sentiment analysis is an emerging, yet challenging, subtask of the natural language processing problem. It aims to discover the affective state and sentimental change of each person in a conversation, and has attracted an increasing attention from both academia and industry. Existing sentiment analysis approaches are insufficient in modelling the interactions among people. However, the development of new approaches are critically limited by the lack of labelled interactive sentiment datasets. In this paper, we present a new conversational database that we have created and made publicly available, namely ScenarioSA, for interactive sentiment analysis. We manually label 2,214 multi-turn English conversations collected from various websites that provide online communication services. In comparison with existing sentiment datasets, ScenarioSA (1) is no longer limited to one specific domain but covers a wide range of topics and scenarios; (2) describes the interactions between two speakers of each conversation; and (3) reflects the sentimental evolution of each speaker over the course of a conversation. Finally, we propose an extension of interactive attention networks that could model the interactions, and compare various strong sentiment analysis algorithms on ScenarioSA, demonstrating the need of novel interactive sentiment analysis models and the potential of ScenarioSA to facilitate the development of such models.

Affective Interaction: Attentive Representation Learning for Multi-Modal Sentiment Classification

Abstract: The recent booming of artificial intelligence (AI) applications, e.g., affective robots, human-machine interfaces, autonomous vehicles, and so on, has produced a great number of multi-modal records of human communication. Such data often carry latent subjective users’ attitudes and opinions, which provides a practical and feasible path to realize the connection between human emotion and intelligence services. Sentiment and emotion analysis of multi-modal records is of great value to improve the intelligence level of affective services. However, how to find an optimal manner to learn people’s sentiments and emotional representations has been a difficult problem, since both of them involve subtle mind activity. To solve this problem, a lot of approaches have been published, but most of them are insufficient to mine sentiment and emotion, since they have treated sentiment analysis and emotion recognition as two separate tasks. The interaction between them has been neglected, which limits the efficiency of sentiment and emotion representation learning. In this work, emotion is seen as the external expression of sentiment, while sentiment is the essential nature of emotion. We thus argue that they are strongly related to each other where one’s judgment helps the decision of the other. The key challenges are multi-modal fused representation and the interaction between sentiment and emotion. To solve such issues, we design an external knowledge enhanced multi-task representation learning network, termed KAMT. The major elements contain two attention mechanisms, which are inter-modal and inter-task attentions and an external knowledge augmentation layer. The external knowledge augmentation layer is used to extract the vector of the participant’s gender, age, occupation, and of overall color or shape. The main use of inter-modal attention is to capture effective multi-modal fused features. Inter-task attention is designed to model the correlation between sentiment analysis and emotion classification. We perform experiments on three widely used datasets, and the experimental performance proves the effectiveness of the KAMT model.

Real-Time Video Emotion Recognition based on Reinforcement Learning and Domain Knowledge

Abstract: Multimodal emotion recognition in conversational videos (ERC) develops rapidly in recent years. To fully extract the relative context from video clips, most studies build their models on the entire dialogues which make them lack of real-time ERC ability. Different from related researches, a novel multimodal emotion recognition model for conversational videos based on reinforcement learning and domain knowledge (ERLDK) is proposed in this paper. In ERLDK, the reinforcement learning algorithm is introduced to conduct real-time ERC with the occurrence of conversations. The collection of history utterances is composed as an emotion-pair which represents the multimodal context of the following utterance to be recognized. Dueling deep-Q-network (DDQN) based on gated recurrent unit (GRU) layers is designed to learn the correct action from the alternative emotion categories. Domain knowledge is extracted from public dataset based on the former information of emotion-pairs. The extracted domain knowledge is used to revise the results from the RL module and is transformed into other dataset to examine the rationality. The experimental results on datasets show that ERLDK achieves the state-of-the-art results on weighted average and most of the specific emotion categories.

CFN: A Complex-valued Fuzzy Network for Sarcasm Detection in Conversations

Abstract: Sarcasm detection in conversation (SDC), a theoretically and practically challenging artificial intelligence (AI) task, aims to discover elusively ironic, contemptuous and metaphoric information implied in daily conversations. Most of the recent approaches in sarcasm detection have neglected the intrinsic vagueness and uncertainty of human language in emotional expression and understanding. To address this gap, we propose a complex-valued fuzzy network (CFN) by leveraging the mathematical formalisms of quantum theory (QT) and fuzzy logic. In particular, the target utterance to be recognized is considered as a quantum superposition of a set of separate words. The contextual interaction between adjacent utterances is described as the interaction between a quantum system and its surrounding environment, constructing the quantum composite system, where the weight of interaction is determined by a fuzzy membership function. In order to model both the vagueness and uncertainty, the aforementioned superposition and composite systems are mathematically encapsulated in a density matrix. Finally, a quantum fuzzy measurement is performed on the density matrix of each utterance to yield the probabilistic outcomes of sarcasm recognition. Extensive experiments are conducted on the MUStARD and the 2020 sarcasm detection Reddit track datasets, and the results show that our model outperforms a wide range of strong baselines.