Compared to traditional sentiment analysis, which only considers text, multimodal sentiment analysis needs to consider emotional signals from multimodal sources simultaneously and is therefore more consistent with the way how humans process sentiment in real-world scenarios. It involves processing emotional information from various sources such as natural language, images, videos, audio, physiological signals, etc. However, although other modalities also contain diverse emotional cues, natural language usually contains richer contextual information and therefore always occupies a crucial position in multimodal sentiment analysis. The emergence of ChatGPT has opened up immense potential for applying large language models (LLMs) to text-centric multimodal tasks. However, it is still unclear how existing LLMs can adapt better to text-centric multimodal sentiment analysis tasks. This survey aims to (1) present a comprehensive review of recent research in text-centric multimodal sentiment analysis tasks, (2) examine the potential of LLMs for text-centric multimodal sentiment analysis, outlining their approaches, advantages, and limitations, (3) summarize the application scenarios of LLM-based multimodal sentiment analysis technology, and (4) explore the challenges and potential research directions for multimodal sentiment analysis in the future.
Social Media users tend to mention entities when reacting to news events. The main purpose of this work is to create entity-centric aggregations of tweets on a daily basis. By applying topic modeling and sentiment analysis, we create data visualization insights about current events and people reactions to those events from an entity-centric perspective.
Camellia: Benchmarking Cultural Biases in LLMs for Asian Languages
Georgia Institute of Technology（佐治亚理工学院）；Samsung R&D Institute Philippines（三星菲律宾研发院）；Sungkyunkwan University（成均馆大学）；Tohoku University（东北大学）；National University of Singapore（新加坡国立大学）；University of Copenhagen（哥本哈根大学）；University of Michigan（密歇根大学）；Indian Institute of Technology Delhi（印度理工学院德里）；Institute of Science Tokyo（东京科学研究院）
Westlake University（西湖大学）；Westlake Institute for Advanced Study（西湖高等研究院）；Zhejiang University（浙江大学）；Tencent SPPD（腾讯可持续社会价值事业部）
Sentiment analysis provides a useful overview of customer review contents. Many review websites allow a user to enter a summary in addition to a full review. Intuitively, summary information may give additional benefit for review sentiment analysis. In this paper, we conduct a study to exploit methods for better use of summary information. We start by finding out that the sentimental signal distribution of a review and that of its corresponding summary are in fact complementary to each other. We thus explore various architectures to better guide the interactions between the two and propose a h…
Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages
Saarland University（萨尔兰大学）；Luleå University of Technology（吕勒奥理工大学）；Instituto Politécnico Nacional（国家理工学院）；Montclair State University（蒙特克莱尔州立大学）；University of Ibadan（伊巴丹大学）；Iowa State University（爱荷华州立大学）；Masakhane NLP（Masakhane自然语言处理）；Brandeis University（布兰迪斯大学）
A Multi-Task Text Classification Pipeline with Natural Language Explanations: A User-Centric Evaluation in Sentiment Analysis and Offensive Language Identification in Greek Tweets
Institute for Language, Cognition and Computation（语言、认知与计算研究所）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）
We present a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision (e.g., in the form of product domain labels and user-provided ratings). Our method combines two weakly supervised components to identify salient opinions and form extractive summaries from multiple reviews: an aspect extractor trained under a multi-task objective, and a sentiment predictor based on multiple instance learning. We introduce an opinion summarization dataset that includes a training set of product reviews from six diverse domains and human…
Large Language Models for Power Scheduling: A User-Centric Approach
Université de Lorraine（洛林大学）；Khalifa University（哈利法大学）；Ecole Polytechnique（巴黎综合理工学院）；Central South University（中南大学）；Open Innovation AI（开放创新人工智能公司）；TII（技术创新研究所）；KU 6G Research Center（KU 6G研究中心）
NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis
Bosch Center for Artificial Intelligence（博世人工智能中心）；Center for Information and Language Processing (CIS), LMU Munich（慕尼黑大学语言信息处理中心）；Karlsruhe University of Applied Sciences（卡尔斯鲁厄应用科学大学）
The rapid growth in data on the internet requires a data mining process to reach a decision to support insight. The Persian language has strong potential for deep research in any aspect of natural language processing, especially sentimental analysis approach. Thousands of websites and blogs updates and modifies by Persian users around the world that contains millions of Persian context. This range of application requires a comprehensive structured framework to extract beneficial information for helping enterprises to enhance their business and initiate a customer-centric management process by …
The human language can be expressed through multiple sources of information known as modalities, including tones of voice, facial gestures, and spoken language. Recent multimodal learning with strong performances on human-centric tasks such as sentiment analysis and emotion recognition are often black-box, with very limited interpretability. In this paper we propose Multimodal Routing, which dynamically adjusts weights between input modalities and output representations differently for each input sample. Multimodal routing can identify relative importance of both individual modalities and cros…
Recent advances in natural language processing have enabled automation of a wide range of tasks, including machine translation, named entity recognition, and sentiment analysis. Automated summarization of documents, or groups of documents, however, has remained elusive, with many efforts limited to extraction of keywords, key phrases, or key sentences. Accurate abstractive summarization has yet to be achieved due to the inherent difficulty of the problem, and limited availability of training data. In this paper, we propose a topic-centric unsupervised multi-document summarization framework to …
Zhejiang University（浙江大学）；Alibaba-Zhejiang University Frontier Technology Research Center（阿里巴巴-浙江大学前沿技术研究中心）；The First Affiliated Hospital of Zhejiang University（浙江大学医学院附属第一医院）；Alibaba Group（阿里巴巴集团）；Carnegie Mellon University（卡内基梅隆大学）；Oxford University（牛津大学）
Text classification tends to be difficult when data are deficient or when it is required to adapt to unseen classes. In such challenging scenarios, recent studies have often used meta-learning to simulate the few-shot task, thus negating implicit common linguistic features across tasks. This paper addresses such problems using meta-learning and unsupervised language models. Our approach is based on the insight that having a good generalization from a few examples relies on both a generic model initialization and an effective strategy for adapting this model to newly arising tasks. We show that…
Massachusetts Institute of Technology（麻省理工学院）；Google Research（谷歌研究院）
How can we train a dialog model to produce better conversations by learning from human feedback, without the risk of humans teaching it harmful chat behaviors? We start by hosting models online, and gather human feedback from real-time, open-ended conversations, which we then use to train and improve the models using offline reinforcement learning (RL). We identify implicit conversational cues including language similarity, elicitation of laughter, sentiment, and more, which indicate positive human feedback, and embed these in multiple reward functions. A well-known challenge is that learning …
Fair Play in the Newsroom: Actor-Based Filtering Gender Discrimination in Text Corpora
Faculty for Computer Science and Mathematics, Hochschule München University of Applied Sciences（慕尼黑应用科学大学计算机科学与数学学院）；Department of Statistics, LMU Munich（慕尼黑大学统计学系）；Munich Center for Machine Learning (MCML), LMU Munich（慕尼黑大学机器学习中心）
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Technische Universität Berlin（柏林工业大学）
The human-centric explainable artificial intelligence (HCXAI) community has raised the need for framing the explanation process as a conversation between human and machine. In this position paper, we establish desiderata for Mediators, text-based conversational agents which are capable of explaining the behavior of neural models interactively using natural language. From the perspective of natural language processing (NLP) research, we engineer a blueprint of such a Mediator for the task of sentiment analysis and assess how far along current research is on the path towards dialogue-based expla…
