The impact of culture in visual emotion perception has recently captured the attention of multimedia research. In this study, we pro- vide powerful computational linguistics tools to explore, retrieve and browse a dataset of 16K multilingual affective visual concepts and 7.3M Flickr images. First, we design an effective crowdsourc- ing experiment to collect human judgements of sentiment connected to the visual concepts. We then use word embeddings to repre- sent these concepts in a low dimensional vector space, allowing us to expand the meaning around concepts, and thus enabling insight about commonalities and differences among different languages. We compare a variety of concept representations through a novel evaluation task based on the notion of visual semantic relatedness. Based on these representations, we design clustering schemes to group multilingual visual concepts, and evaluate them with novel metrics based on the crowdsourced sentiment annotations as well as visual semantic relatedness. The proposed clustering framework enables us to analyze the full multilingual dataset in-depth and also show an application on a facial data subset, exploring cultural in- sights of portrait-related affective visual concepts.
Columbia University（哥伦比亚大学）；Idiap Research Institute（伊迪亚普研究所）；EPFL（洛桑联邦理工学院）；Yahoo Labs（雅虎实验室）；JW Player（JW Player公司）
Every culture and language is unique. Our work expressly focuses on the uniqueness of culture and language in relation to human affect, specifically sentiment and emotion semantics, and how they manifest in social multimedia. We develop sets of sentiment- and emotion-polarized visual concepts by adapting semantic structures called adjective-noun pairs, originally introduced by Borth et al. (2013), but in a multilingual context. We propose a new language-dependent method for automatic discovery of these adjective-noun constructs. We show how this pipeline can be applied on a social multimedia p…
This technical report details several improvements to the visual concept detector banks built on images from the Multilingual Visual Sentiment Ontology (MVSO). The detector banks are trained to detect a total of 9,918 sentiment-biased visual concepts from six major languages: English, Spanish, Italian, French, German and Chinese. In the original MVSO release, adjective-noun pair (ANP) detectors were trained for the six languages using an AlexNet-styled architecture by fine-tuning from DeepSentiBank. Here, through a more extensive set of experiments, parameter tuning, and training runs, we deta…
Language models are ubiquitous in current NLP, and their multilingual capacity has recently attracted considerable attention. However, current analyses have almost exclusively focused on (multilingual variants of) standard benchmarks, and have relied on clean pre-training and task-specific corpora as multilingual signals. In this paper, we introduce XLM-T, a model to train and evaluate multilingual language models in Twitter. In this paper we provide: (1) a new strong multilingual baseline consisting of an XLM-R (Conneau et al. 2020) model pre-trained on millions of tweets in over thirty langu…
NTIS – New Technologies for the Information Society（NTIS – 新信息技术学会）；University of West Bohemia（西波希米亚大学）
In this paper, we aim at improving Czech sentiment with transformer-based models and their multilingual versions. More concretely, we study the task of polarity detection for the Czech language on three sentiment polarity datasets. We fine-tune and perform experiments with five multilingual and three monolingual models. We compare the monolingual and multilingual models' performance, including comparison with the older approach based on recurrent neural networks. Furthermore, we test the multilingual models and their ability to transfer knowledge from English to Czech (and vice versa) with zer…
University of the Basque Country（巴斯克大学）；Universidade Federal de Goiás（戈亚斯联邦大学）；University of Amsterdam（阿姆斯特丹大学）
Hashtag segmentation, also known as hashtag decomposition, is a common step in preprocessing pipelines for social media datasets. It usually precedes tasks such as sentiment analysis and hate speech detection. For sentiment analysis in medium to low-resourced languages, previous research has demonstrated that a multilingual approach that resorts to machine translation can be competitive or superior to previous approaches to the task. We develop a zero-shot hashtag segmentation framework and demonstrate how it can be used to improve the accuracy of multilingual sentiment analysis pipelines. Our…
Insight Centre for Data Analytics（数据洞察中心）；National University of Ireland, Galway（爱尔兰国立大学戈尔韦分校）；Aylien Ltd.（Aylien有限公司）
This paper describes our deep learning-based approach to multilingual aspect-based sentiment analysis as part of SemEval 2016 Task 5. We use a convolutional neural network (CNN) for both aspect extraction and aspect-based sentiment analysis. We cast aspect extraction as a multi-label classification problem, outputting probabilities over aspects parameterized by a threshold. To determine the sentiment towards an aspect, we concatenate an aspect vector with every word embedding and apply a convolution over it. Our constrained system (unconstrained for English) achieves competitive results across…
University of Helsinki（赫尔辛基大学）；École Normale Supérieure（巴黎高等师范学院）
We present a method for extracting a multilingual sentiment annotated dialog data set from Fallout New Vegas. The game developers have preannotated every line of dialog in the game in one of the 8 different sentiments: \textit{anger, disgust, fear, happy, neutral, pained, sad } and \textit{surprised}. The game has been translated into English, Spanish, German, French and Italian. We conduct experiments on multilingual, multilabel sentiment analysis on the extracted data set using multilingual BERT, XLMRoBERTa and language specific BERT models. In our experiments, multilingual BERT outperformed…
We introduce XED, a multilingual fine-grained emotion dataset. The dataset consists of human-annotated Finnish (25k) and English sentences (30k), as well as projected annotations for 30 additional languages, providing new resources for many low-resource languages. We use Plutchik's core emotions to annotate the dataset with the addition of neutral to create a multilabel multiclass dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to show that XED performs on par with other similar datasets and is therefore a useful tool for sentiment analysis and emotion …
Although previous research on Aspect-based Sentiment Analysis (ABSA) for Indonesian reviews in hotel domain has been conducted using CNN and XGBoost, its model did not generalize well in test data and high number of OOV words contributed to misclassification cases. Nowadays, most state-of-the-art results for wide array of NLP tasks are achieved by utilizing pretrained language representation. In this paper, we intend to incorporate one of the foremost language representation model, BERT, to perform ABSA in Indonesian reviews dataset. By combining multilingual BERT (m-BERT) with task transforma…
UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment Analysis
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
With increasing globalization and immigration, various studies have estimated that about half of the world population is bilingual. Consequently, individuals concurrently use two or more languages or dialects in casual conversational settings. However, most research is natural language processing is focused on monolingual text. To further the work in code-switched sentiment analysis, we propose a multi-step natural language processing algorithm utilizing points of code-switching in mixed text and conduct sentiment analysis around those identified points. The proposed sentiment analysis algorit…
Sentiment analysis is a widely studied NLP task where the goal is to determine opinions, emotions, and evaluations of users towards a product, an entity or a service that they are reviewing. One of the biggest challenges for sentiment analysis is that it is highly language dependent. Word embeddings, sentiment lexicons, and even annotated data are language specific. Further, optimizing models for each language is very time consuming and labor intensive especially for recurrent neural network models. From a resource perspective, it is very challenging to collect data for different languages. In…
Wrocław University of Science and Technology（弗罗茨瓦夫理工大学）；Brand24 AI（Brand24人工智能公司）
Models are increasing in size and complexity in the hunt for SOTA. But what if those 2\% increase in performance does not make a difference in a production use case? Maybe benefits from a smaller, faster model outweigh those slight performance gains. Also, equally good performance across languages in multilingual tasks is more important than SOTA results on a single one. We present the biggest, unified, multilingual collection of sentiment analysis datasets. We use these to assess 11 models and 80 high-quality sentiment datasets (out of 342 raw datasets collected) in 27 languages and included …
M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis
Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials（广东省量子工程与量子材料重点实验室）；South China Normal University（华南师范大学）；LMU Munich（慕尼黑大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Technical University of Munich（慕尼黑工业大学）；Renmin University of China（中国人民大学）；Brown University（布朗大学）；ShanghaiTech University（上海科技大学）；FAU Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）
An increase in the use of smartphones has laid to the use of the internet and social media platforms. The most commonly used social media platforms are Twitter, Facebook, WhatsApp and Instagram. People are sharing their personal experiences, reviews, feedbacks on the web. The information which is available on the web is unstructured and enormous. Hence, there is a huge scope of research on understanding the sentiment of the data available on the web. Sentiment Analysis (SA) can be carried out on the reviews, feedbacks, discussions available on the web. There has been extensive research carried…
The surge of social media use brings huge demand of multilingual sentiment analysis (MSA) for unveiling cultural difference. So far, traditional methods resorted to machine translation---translating texts in other languages to English, and then adopt the methods once worked in English. However, this paradigm is conditioned by the quality of machine translation. In this paper, we propose a new deep learning paradigm to assimilate the differences between languages for MSA. We first pre-train monolingual word embeddings separately, then map word embeddings in different spaces into a shared embedd…
The evolution of the Internet has increased the amount of information that is expressed by people on different platforms. This information can be product reviews, discussions on forums, or social media platforms. Accessibility of these opinions and peoples feelings open the door to opinion mining and sentiment analysis. As language and speech technologies become more advanced, many languages have been used and the best models have been obtained. However, due to linguistic diversity and lack of datasets, African languages have been left behind. In this study, by using the current state-of-the-a…
Ensembling Multilingual Transformers for Robust Sentiment Analysis of Tweets
Department of Computer Science, University of South Carolina, USA（南卡罗来纳大学计算机科学系）；Artificial Intelligence Institute, University of South Carolina, USA（南卡罗来纳大学人工智能研究所）；Department of Civil, Environmental, and Ocean Engineering, Stevens Institute of Technology, New Jersey, USA（史蒂文斯理工学院土木、环境与海洋工程系）；School of Computing, University of Nebraska–Lincoln, Lincoln, Nebraska, USA（内布拉斯加大学林肯分校计算机学院）；Department of Computer Science, Bowling Green State University, Bowling Green, Ohio, USA（伯灵顿州立大学计算机科学系）
As natural language processing systems become more widespread, it is necessary to address fairness issues in their implementation and deployment to ensure that their negative impacts on society are understood and minimized. However, there is limited work that studies fairness using a multilingual and intersectional framework or on downstream tasks. In this paper, we introduce four multilingual Equity Evaluation Corpora, supplementary test sets designed to measure social biases, and a novel statistical framework for studying unisectional and intersectional social biases in natural language proc…
Tweets are specific text data when compared to general text. Although sentiment analysis over tweets has become very popular in the last decade for English, it is still difficult to find huge annotated corpora for non-English languages. The recent rise of the transformer models in Natural Language Processing allows to achieve unparalleled performances in many tasks, but these models need a consequent quantity of text to adapt to the tweet domain. We propose the use of a multilingual transformer model, that we pre-train over English tweets and apply data-augmentation using automatic translation…
LIAAD - INESC TEC；Faculty of Sciences-University of Porto（波尔图大学理学院）；Saarland University（萨尔大学）；Google Research（谷歌研究院）；Bayero University（巴耶罗大学）；Ahmadu Bello University（艾哈迈杜·贝罗大学）；Microsoft Research India（微软研究院印度）；Technical University of Munich（慕尼黑工业大学）；Clear Global；Kaduna state University（卡杜纳州立大学）
Sentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data. We introduce the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria (Hausa, Igbo, Nigerian-Pidgin, and Yorùbá ) consisting of around 30,000 annotated tweets per language (and 14,000 for Nigerian-Pidgin), including a significant fraction of code-mixed tweets. We propose text collection, filtering, processing and labeling methods that enable us to create datasets for these low-resource languages. We …
ICT-NLP at SemEval-2026 Task 3: Less Is More -- Multilingual Encoder with Joint Training and Adaptive Ensemble for Dimensional Aspect Sentiment Regression
State Key Laboratory of AI Safety（人工智能安全国家重点实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
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）
University College Groningen, University of Groningen（格罗宁根大学学院，格罗宁根大学）；Foundation The London Story（伦敦故事基金会）
In recent years, there has been a heightened consensus within academia and in the public discourse that Social Media Platforms (SMPs), amplify the spread of hateful and negative sentiment content. Researchers have identified how hateful content, political propaganda, and targeted messaging contributed to real-world harms including insurrections against democratically elected governments, genocide, and breakdown of social cohesion due to heightened negative discourse towards certain communities in parts of the world. To counter these issues, SMPs have created semi-automated systems that can hel…
What are the limits of automated Twitter sentiment classification? We analyze a large set of manually labeled tweets in different languages, use them as training data, and construct automated classification models. It turns out that the quality of classification models depends much more on the quality and size of training data than on the type of the model trained. Experimental results indicate that there is no statistically significant difference between the performance of the top classification models. We quantify the quality of training data by applying various annotator agreement measures,…
Evaluating Zero-Shot Multilingual Aspect-Based Sentiment Analysis with Large Language Models
Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, School of Electronic Science and Engineering (School of Microelectronics)（广东量子工程与材料重点实验室，电子科学与工程学院（微电子学院））；South China Normal University（华南师范大学）；Guangdong Provincial Key Laboratory of Intelligent Information Processing（广东智能信息处理重点实验室）；Ludwig Maximilian University of Munich（慕尼黑路德维希-马克西米利安大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Technical University of Munich（慕尼黑技术大学）
In this work, we take the first steps towards building a universal rewriter: a model capable of rewriting text in any language to exhibit a wide variety of attributes, including styles and languages, while preserving as much of the original semantics as possible. In addition to obtaining state-of-the-art results on unsupervised translation, we also demonstrate the ability to do zero-shot sentiment transfer in non-English languages using only English exemplars for sentiment. We then show that our model is able to modify multiple attributes at once, for example adjusting both language and sentim…
CONACyT（墨西哥国家科学技术委员会）；INFOTEC（墨西哥信息技术研究与开发中心）；Centro de Investigación en Geografía y Geomática “Ing. Jorge L. Tamayo”, A.C.（豪尔赫·L·塔马约工程地理与测绘研究中心）；Universidad Michoacana de San Nicolás de Hidalgo（圣尼古拉斯·伊达尔戈米却肯大学）
Recently, sentiment analysis has received a lot of attention due to the interest in mining opinions of social media users. Sentiment analysis consists in determining the polarity of a given text, i.e., its degree of positiveness or negativeness. Traditionally, Sentiment Analysis algorithms have been tailored to a specific language given the complexity of having a number of lexical variations and errors introduced by the people generating content. In this contribution, our aim is to provide a simple to implement and easy to use multilingual framework, that can serve as a baseline for sentiment …
NCL-BU at SemEval-2026 Task 3: Fine-tuning XLM-RoBERTa for Multilingual Dimensional Sentiment Regression
Independent Researcher（独立研究者）；Centre for Applied Creative Technologies (CFACT+), Bournemouth University, UK（应用创意技术中心（CFACT+），伯恩茅斯大学，英国）；School of Computing, Newcastle University, Newcastle upon Tyne, UK（计算学院，新castle大学，新castle upon Tyne，英国）
DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis
National Yang Ming Chiao Tung University；Yuan Ze University；Moscow State University；Skoltech；AIRI；Yunnan University；University of Cincinnati；University of Göttingen；Imperial College London；National Research Council Canada
National University Of Ireland Galway（爱尔兰国立高威大学）；Trinity college Dublin（都柏林圣三一学院）；Eastern University（东方大学）
Social media has penetrated into multilingual societies, however most of them use English to be a preferred language for communication. So it looks natural for them to mix their cultural language with English during conversations resulting in abundance of multilingual data, call this code-mixed data, available in todays' world.Downstream NLP tasks using such data is challenging due to the semantic nature of it being spread across multiple languages.One such Natural Language Processing task is sentiment analysis, for this we use an auto-regressive XLNet model to perform sentiment analysis on co…
INFOTEC Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación（INFOTEC 信息与通信技术研究创新中心）；CentroGEO Centro de Investigación en Ciencias de Información Geoespacial（CentroGEO 地理空间信息科学研究中心）；CONACyT Consejo Nacional de Ciencia y Tecnología（CONACyT 国家科学技术委员会）
Sentiment analysis (SA) is a task related to understanding people's feelings in written text; the starting point would be to identify the polarity level (positive, neutral or negative) of a given text, moving on to identify emotions or whether a text is humorous or not. This task has been the subject of several research competitions in a number of languages, e.g., English, Spanish, and Arabic, among others. In this contribution, we propose an SA system, namely EvoMSA, that unifies our participating systems in various SA competitions, making it domain independent and multilingual by processing …
