Multimodal Sentiment Analysis Based on Deep Learning: Recent Progress

Multimodal sentiment analysis is an important research topic in the field of NLP, aiming to analyze speakers\u27 sentiment tendencies through features extracted from textual, visual, and acoustic modalities. Its main methods are based on machine learning and deep learning. Machine learning-based methods rely heavily on labeled data. But deep learning-based methods can overcome this shortcoming and capture the in-depth semantic information and modal characteristics of the data, as well as the interactive information between multimodal data. In this paper, we survey the deep learning-based methods, including fusion of text and image and fusion of text, image, audio, and video. Specifically, we discuss the main problems of these methods and the future directions. Finally, we review the work of multimodal sentiment analysis in conversation

Accurate emotion strength assessment for seen and unseen speech based on data-driven deep learning

Emotion classification of speech and assessment of the emotion strength are required in applications such as emotional text-to-speech and voice conversion. The emotion attribute ranking function based on Support Vector Machine (SVM) was proposed to predict emotion strength for emotional speech corpus. However, the trained ranking function doesn't generalize to new domains, which limits the scope of applications, especially for out-of-domain or unseen speech. In this paper, we propose a data-driven deep learning model, i.e. StrengthNet, to improve the generalization of emotion strength assessment for seen and unseen speech. This is achieved by the fusion of emotional data from various domains. We follow a multi-task learning network architecture that includes an acoustic encoder, a strength predictor, and an auxiliary emotion predictor. Experiments show that the predicted emotion strength of the proposed StrengthNet is highly correlated with ground truth scores for both seen and unseen speech. We release the source codes at: https://github.com/ttslr/StrengthNet

Productivity Measurement of Call Centre Agents using a Multimodal Classification Approach

Call centre channels play a cornerstone role in business communications and transactions, especially in challenging business situations. Operations’ efficiency, service quality, and resource productivity are core aspects of call centres’ competitive advantage in rapid market competition. Performance evaluation in call centres is challenging due to human subjective evaluation, manual assortment to massive calls, and inequality in evaluations because of different raters. These challenges impact these operations' efficiency and lead to frustrated customers. This study aims to automate performance evaluation in call centres using various deep learning approaches. Calls recorded in a call centre are modelled and classified into high- or low-performance evaluations categorised as productive or nonproductive calls. The proposed conceptual model considers a deep learning network approach to model the recorded calls as text and speech. It is based on the following: 1) focus on the technical part of agent performance, 2) objective evaluation of the corpus, 3) extension of features for both text and speech, and 4) combination of the best accuracy from text and speech data using a multimodal structure. Accordingly, the diarisation algorithm extracts that part of the call where the agent is talking from which the customer is doing so. Manual annotation is also necessary to divide the modelling corpus into productive and nonproductive (supervised training). Krippendorff’s alpha was applied to avoid subjectivity in the manual annotation. Arabic speech recognition is then developed to transcribe the speech into text. The text features are the words embedded using the embedding layer. The speech features make several attempts to use the Mel Frequency Cepstral Coefficient (MFCC) upgraded with Low-Level Descriptors (LLD) to improve classification accuracy. The data modelling architectures for speech and text are based on CNNs, BiLSTMs, and the attention layer. The multimodal approach follows the generated models to improve performance accuracy by concatenating the text and speech models using the joint representation methodology. The main contributions of this thesis are: • Developing an Arabic Speech recognition method for automatic transcription of speech into text. • Drawing several DNN architectures to improve performance evaluation using speech features based on MFCC and LLD. • Developing a Max Weight Similarity (MWS) function to outperform the SoftMax function used in the attention layer. • Proposing a multimodal approach for combining the text and speech models for best performance evaluation

Deep learning-based EEG emotion recognition: Current trends and future perspectives

Automatic electroencephalogram (EEG) emotion recognition is a challenging component of human–computer interaction (HCI). Inspired by the powerful feature learning ability of recently-emerged deep learning techniques, various advanced deep learning models have been employed increasingly to learn high-level feature representations for EEG emotion recognition. This paper aims to provide an up-to-date and comprehensive survey of EEG emotion recognition, especially for various deep learning techniques in this area. We provide the preliminaries and basic knowledge in the literature. We review EEG emotion recognition benchmark data sets briefly. We review deep learning techniques in details, including deep belief networks, convolutional neural networks, and recurrent neural networks. We describe the state-of-the-art applications of deep learning techniques for EEG emotion recognition in detail. We analyze the challenges and opportunities in this field and point out its future directions

Multimodal Argument Mining: A Case Study in Political Debates

We propose a study on multimodal argument mining in the domain of political debates. We collate and extend existing corpora and provide an initial empirical study on multimodal architectures, with a special emphasis on input encoding methods. Our results provide interesting indications about future directions in this important domain

Towards Better Understanding of Spoken Conversations: Assessment of Emotion and Sentiment

Emotions play a vital role in our daily life as they help us convey information impossible to express verbally to other parties. While humans can easily perceive emotions, these are notoriously difficult to define and recognize by machines. However, automatically detecting the emotion of a spoken conversation can be useful for a diverse range of applications such as human-machine interaction and conversation analysis. In this thesis, we present several approaches based on machine learning to recognize emotion from isolated utterances and long recordings. Isolated utterances are usually shorter than 10s in duration and are assumed to contain only one major emotion. One of the main obstacles in achieving high emotion recognition accuracy is the lack of large annotated data. We propose to mitigate this problem by using transfer learning and data augmentation techniques. We show that x-vector representations extracted from speaker recognition models (x-vector models) contain emotion predictive information and adapting those models provide significant improvements in emotion recognition performance. To further improve the performance, we propose a novel perceptually motivated data augmentation method, Copy-Paste on isolated utterances. This method is based on the assumption that the presence of emotions other than neutral dictates a speaker ’s overall perceived emotion in a recording. As isolated utterances are assumed to contain only one emotion, the proposed models make predictions on the utterance level. However, these models can not be directly applied to conversations that can have multiple emotions unless we know the locations of emotion boundaries. In this work, we propose to recognize emotions in the conversations by doing frame-level classification where predictions are made at regular intervals. We compare models trained on isolated utterances and conversations. We propose a data augmentation method, DiverseCatAugment based on attention operation to improve the transformer models. To further improve the performance, we incorporate the turn-taking structure of the conversations into our models. Annotating utterances with emotions is not a simple task and it depends on the number of emotions used for annotation. However, annotation schemes can be changed to reduce annotation efforts based on application. We consider one such application: predicting customer satisfaction (CSAT) in a call center conversation where the goal is to predict the overall sentiment of the customer. We conduct a comprehensive search for adequate acoustic and lexical representations at different granular levels of conversations. We show that the methods that use transfer learning (x-vectors and CSAT Tracker) perform best. Our error analysis shows that the calls where customers accomplished their goal but were still dissatisfied are the most difficult to predict correctly, and the customer’s speech is more emotional compared to the agent’s speech