Critical Discourse Analysis (CDA), a new branch of modern linguistic researches rose abroad in recent years, aims to reveal the interrelationship among language, ideology and power. During the thirty years’ development process, studies of CDA present different characteristics in different phases. So far, both in China and abroad, great achievements concerning CDA has been made. Through reviewing the multiple perspective studies of CDA at home and abroad, classical theories and analytical approaches related to CDA are elaborated, some new trends of CDA are also discussed and explained. The aim of this paper is to help scholars to get a comprehensive understanding of the development of CDA, with the ultimate purpose of promoting related academic researches.

A Review on Critical Discourse Analysis

A Review of Key Technologies for Emotion Analysis Using Multimodal Information

Most existing audio-text emotion recognition studies have focused on the computational modeling aspects, including strategies for fusing the modalities. An area that has received less attention is understanding the role of proper temporal synchronization between the modalities in the model performance. This study presents a transformer-based model designed with a word-chunk concept, which offers an ideal framework to explore different strategies to align text and speech. The approach creates chunks with alternative alignment strategies with different levels of dependency on the underlying lexical boundaries. A key contribution of this study is the multi-scale chunk alignment strategy, which generates random alignments to create the chunks without considering lexical boundaries. For every epoch, the approach generates a different alignment for each sentence, serving as an effective regularization method for temporal dependency. Our experimental results based on the MSP-Podcast corpus indicate that providing precise temporal alignment information to create the audio-text chunks does not improve the performance of the system. The attention mechanisms in the transformer-based approach are able to compensate for imperfect synchronization between the modalities. However, using exact lexical boundaries makes the system highly vulnerable to missing modalities. In contrast, the model trained with the proposed multi-scale chunk regularization strategy using random alignment can significantly increase its robustness against missing data and remain effective, even under a single audio-only emotion recognition task. The code is available at: https://github.com/winston-lin-wei-cheng/MultiScale-Chunk-Regularization 

Enhancing Resilience to Missing Data in Audio-Text Emotion Recognition with Multi-Scale Chunk Regularization

Human Multimodal Sentiment Analysis (MSA) is an attractive research that studies sentiment expressed from multiple heterogeneous modalities. While transformer-based methods have achieved great success, designing an effective “co-attention” model to associate text modality with nonverbal modalities remains challenging. There are two main problems: 1) the dominant role of the text in modalities is underutilization, and 2) the interaction between modalities is not sufficiently explored. This paper proposes a deep modular Co-Attention Shifting Network (CoASN) for MSA. A Cross-modal Modulation Module based on Co-attention (CMMC) and an Advanced Modality-mixing Adaptation Gate (AMAG) are constructed. The CMMC consists of the Text-guided Co-Attention (TCA) and Interior Transformer Encoder (ITE) units to capture inter-modal features and intra-modal features. With text modality as the core, the CMMC module aims to guide and promote the expression of emotion in nonverbal modalities, and the nonverbal modalities increase the richness of the text-based multimodal sentiment information. In addition, the AMAG module is introduced to explore the dynamical correlations among all modalities. Particularly, this efficient module first captures the nonverbal shifted representations and then combines them to calculate the shifted word embedding representations for the final MSA tasks. Extensive experiments on two commonly used datasets, CMU-MOSI and CMU-MOSEI, demonstrate that our proposed method is superior to the state-of-the-art performance.

Deep Modular Co-Attention Shifting Network for Multimodal Sentiment Analysis

Generalizing sentiment analysis: a review of progress, challenges, and emerging directions

For multimodal sentiment analysis (MSA), the text-centric approach has been shown to be superior in performance, which adopts powerful text models (e.g., BERT) as backbone and stud-ies how to effectively incorporate non-verbal modalities (i.e., audio and visual) to obtain more reﬁned and expressive word representations. In previous methods, the non-verbal information injected into a word representation only comes from a non-verbal segment corresponding to the time span of the word, ignoring the long-range dependencies across modalities. Meanwhile, these methods utilize the Softmax normalization function-based attention mechanism, which makes it difﬁcult to highlight the important information in non-verbal sequences. To this end, this paper proposes a non-verbal information injection method called Word-wise Sparse Attention (WSA) to capture the cross-modal long-range dependencies. When injecting the non-verbal information into a word, the word is used as the semantic anchor to search for the most relevant non-verbal information from holistic non-verbal sequences. Furthermore, an advanced Multimodal Adaptive Gating (MAG) mechanism is introduced to determine the amount of information injected from non-verbal modalities. We evaluate our method on the two publicly available multimodal sentiment datasets. Experimental results show that the proposed approach improves the baseline model consistently on all metrics.

Word-wise Sparse Attention for Multimodal Sentiment Analysis

Modeling intra-modal and cross-modal interactions poses significant challenges in multimodal sentiment analysis. Currently, graph-based methods like HGraph-CL achieve promising performance, which rely on two different levels of graph contrastive learning within and between modalities to explore sentiment correlations. However, HGraph-CL still faces the following drawbacks in graph construction: 1) nodes of the graph are represented at the frame level, only containing low-level information, neglecting the correlations among high-level semantics; 2) edges of the graph are based on the fixed dependency relations between words in the text sequence and the adjacent relations between frame-level nodes in the non-verbal sequences, failing to effectively capture implicit and long-distance correlations. To this end, this letter introduces capsule networks to construct high-level semantic nodes in a graph, uncovering deep sentimental structures. Furthermore, the learnable adjacency matrices are employed to construct edges of graph, thus adaptively learning the relations between nodes. Experimental results on several benchmark datasets for multimodal sentiment analysis demonstrate the effectiveness of the proposed method.

Capturing High-Level Semantic Correlations via Graph for Multimodal Sentiment Analysis

Critical discourse analysis mainly reveals the relations among language usage,power and ideology by analyzing language characteristics and its social and cultural background.The article analyzed 20 speeches of female politicians based on the website gos.sbc.edu from personal pronouns,modal verbs,and transitivity in order to probe into the character of female politicians' speeches and the difference between female and male politicians.

Critical Discourse Analysis of Female Politicians' Speech

Speech emotion recognition is a challenge and an impor-tant step towards more natural human-computer interaction (HCI). The popular approach is multimodal emotion recognition based on model-level fusion, which means that the multimodal signals can be encoded to acquire embeddings, and then the embeddings are concatenated together for the ﬁnal classiﬁcation. However, due to the inﬂuence of noise or other factors, each modality does not always tend to the same emotional category, which affects the generalization of a model. In this paper, we propose a novel regularization method via contrastive learning for multimodal emotion recognition using audio and text. By introducing a discriminator to distinguish the difference between the same and different emotional pairs, we explicitly restrict the latent code of each modality to contain the same emotional information, so as to reduce the noise interference and get more discrim-inative representation. Experiments are performed on the standard IEMOCAP dataset for 4-class emotion recognition. The results show a signiﬁcant improvement of 1.44% and 1.53% in terms of weighted accuracy (WA) and unweighted accuracy (UA) compared to the baseline system.

Contrastive Regularization for Multimodal Emotion Recognition Using Audio and Text

Multimodal Sentiment Analysis (MSA) has made great progress that benefits from extraordinary fusion scheme. However, there is a lack of labeled data, resulting in severe over-fitting and poor generalization for supervised models applied in this field. In this paper, we propose Sentiment Knowledge Enhanced Self-supervised Learning (SKESL) to capture common sentimental patterns in unlabeled videos, which facilitates further learning on limited labeled data. Specifically, with the help of sentiment knowledge and non-verbal behavior, SKESL conducts sentiment word masking and predicts fine-grained word sentiment intensity, so as to embed sentiment information at the word level into pre-trained multimodal representation. In addition, a non-verbal injection method is also proposed to integrate non-verbal information into the word semantics. Experiments on two standard benchmarks of MSA clearly show that SKESL significantly outperforms the baseline, and achieves new State-Of-The-Art (SOTA) results.

pdf/sentiment-knowledge-enhanced-self-supervised-learning-for-z55jwk75.pdf

Sentiment Knowledge Enhanced Self-supervised Learning for Multimodal Sentiment Analysis

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Fan Qian

Author Tools

Chat about Author