Sentiment classification is a quickly advancing field of study with applications in almost any field. While various models and datasets have shown high accuracy inthe task of binary classification, the task of fine-grained sentiment classification is still an area with room for significant improvement. Analyzing the SST-5 dataset,previous work by Munikar et al. (2019) showed that the embedding tool BERT allowed a simple model to achieve state-of-the-art accuracy. Since that paper, several BERT alternatives have been published, with three primary ones being AlBERT (Lan et al., 2019), DistilBERT (Sanh et al. 2019), and RoBERTa (Liu etal. 2019). While these models report some improvement over BERT on the popular benchmarks GLUE, SQuAD, and RACE, they have not been applied to the fine-grained classification task. In this paper, we examine whether the improvements hold true when applied to a novel task, by replicating the BERT model from Munikar et al., and swapping the embedding layer to the alternative models. Over the experiments, we found that AlBERT suffers significantly more accuracy loss than reported on other tasks, DistilBERT has accuracy loss similar to their reported loss on other tasks while being the fastest model to train, and RoBERTa reaches anew state-of-the-art accuracy for prediction on the SST-5 root level (60.2%).
Multimodal fine-grained sentiment analysis has recently attracted increasing attention due to its broad applications. However, the existing multimodal fine-grained sentiment datasets most focus on annotating the fine-grained elements in text but ignore those in images, which leads to the fine-grained elements in visual content not receiving the full attention they deserve. In this paper, we propose a new dataset, the Multimodal Aspect-Category Sentiment Analysis (MACSA) dataset, which contains more than 21K text-image pairs. The dataset provides fine-grained annotations for both textual and vi…
Fine-grained sentiment analysis attempts to extract sentiment holders, targets and polar expressions and resolve the relationship between them, but progress has been hampered by the difficulty of annotation. Targeted sentiment analysis, on the other hand, is a more narrow task, focusing on extracting sentiment targets and classifying their polarity.In this paper, we explore whether incorporating holder and expression information can improve target extraction and classification and perform experiments on eight English datasets. We conclude that jointly predicting target and polarity BIO labels …
Target-oriented sentiment classification is a fine-grained task of natural language processing to analyze the sentiment polarity of the targets. To improve the performance of sentiment classification, many approaches proposed various attention mechanisms to capture the important context words of a target. However, previous approaches ignored the significant relatedness of a target's sentiment and its local context. This paper proposes a local context-aware network (LCA-Net), equipped with the local context embedding and local context prediction loss, to strengthen the model by emphasizing the …
Access to word-sentiment associations is useful for many applications, including sentiment analysis, stance detection, and linguistic analysis. However, manually assigning fine-grained sentiment association scores to words has many challenges with respect to keeping annotations consistent. We apply the annotation technique of Best-Worst Scaling to obtain real-valued sentiment association scores for words and phrases in three different domains: general English, English Twitter, and Arabic Twitter. We show that on all three domains the ranking of words by sentiment remains remarkably consistent …
Neural sequence models have achieved great success in sentence-level sentiment classification. However, some models are exceptionally complex or based on expensive features. Some other models recognize the value of existed linguistic resource but utilize it insufficiently. This paper proposes a novel and general method to incorporate lexicon information, including sentiment lexicons(+/-), negation words and intensifiers. Words are annotated in fine-grained and coarse-grained labels. The proposed method first encodes the fine-grained labels into sentiment embedding and concatenates it with word…
Tencent Inc.（腾讯公司）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；Baidu Inc.（百度公司）
Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term, sentiment and opinion term triplets from sentences and tries to provide a complete solution for aspect-based sentiment analysis (ABSA). However, some triplets extracted by ASTE are confusing, since the sentiment in a triplet extracted by ASTE is the sentiment that the sentence expresses toward the aspect term rather than the sentiment of the aspect term and opinion term pair. In this paper, we introduce a more fine-grained Aspect-Sentiment-Opinion Triplet Extraction (ASOTE) Task. ASOTE also extracts aspect term, sentiment …
Microblogs have become a social platform for people to express their emotions in real-time, and it is a trend to analyze user emotional tendencies from the information on Microblogs. The dynamic features of emojis can affect the sentiment polarity of microblog texts. Since existing models seldom consider the diversity of emoji sentiment polarity,the paper propose a microblog sentiment classification model based on ALBERT-FAET. We obtain text embedding via ALBERT pretraining model and learn the inter-emoji embedding with an attention-based LSTM network. In addition, a fine-grained attention mec…
We introduce NoReC_fine, a dataset for fine-grained sentiment analysis in Norwegian, annotated with respect to polar expressions, targets and holders of opinion. The underlying texts are taken from a corpus of professionally authored reviews from multiple news-sources and across a wide variety of domains, including literature, games, music, products, movies and more. We here present a detailed description of this annotation effort. We provide an overview of the developed annotation guidelines, illustrated with examples, and present an analysis of inter-annotator agreement. We also report the f…
While the general task of textual sentiment classification has been widely studied, much less research looks specifically at sentiment between a specified source and target. To tackle this problem, we experimented with a state-of-the-art relation extraction model. Surprisingly, we found that despite reasonable performance, the model's attention was often systematically misaligned with the words that contribute to sentiment. Thus, we directly trained the model's attention with human rationales and improved our model performance by a robust 4~8 points on all tasks we defined on our data sets. We…
Text style transfer is usually performed using attributes that can take a handful of discrete values (e.g., positive to negative reviews). In this work, we introduce an architecture that can leverage pre-trained consistent continuous distributed style representations and use them to transfer to an attribute unseen during training, without requiring any re-tuning of the style transfer model. We demonstrate the method by training an architecture to transfer text conveying one sentiment to another sentiment, using a fine-grained set of over 20 sentiment labels rather than the binary positive/nega…
DyFuLM: An Advanced Multimodal Framework for Sentiment Analysis
Department of Applied Mathematics, Xi'an Jiaotong-Liverpool University（应用数学系，西安交通大学-利物浦大学）；School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang)（人工智能与先进计算学院，XJTLU创业学院（太仓））；Department of Intelligent Science, Xi'an Jiaotong-Liverpool University（智能科学系，西安交通大学-利物浦大学）
FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis
School of Artificial Intelligence, Beihang University, China（北京航空航天大学人工智能学院）；School of Computer Science and Engineering, Beihang University, China（北京航空航天大学计算机科学与工程学院）；Xidian University, China（西安电子科技大学）；Zhongguancun Laboratory, China（中关村实验室）
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Opinion summarization aims to profile a target by extracting opinions from multiple documents. Most existing work approaches the task in a semi-supervised manner due to the difficulty of obtaining high-quality annotation from thousands of documents. Among them, some use aspect and sentiment analysis as a proxy for identifying opinions. In this work, we propose a new framework, FineSum, which advances this frontier in three aspects: (1) minimal supervision, where only aspect names and a few aspect/sentiment keywords are available; (2) fine-grained opinion analysis, where sentiment analysis dril…
Traditional sentiment analysis approaches tackle problems like ternary (3-category) and fine-grained (5-category) classification by learning the tasks separately. We argue that such classification tasks are correlated and we propose a multitask approach based on a recurrent neural network that benefits by jointly learning them. Our study demonstrates the potential of multitask models on this type of problems and improves the state-of-the-art results in the fine-grained sentiment classification problem.
Tribhuvan University（特里布万大学）；Institute of Engineering, Tribhuvan University（特里布万大学工程学院）
Sentiment classification is an important process in understanding people's perception towards a product, service, or topic. Many natural language processing models have been proposed to solve the sentiment classification problem. However, most of them have focused on binary sentiment classification. In this paper, we use a promising deep learning model called BERT to solve the fine-grained sentiment classification task. Experiments show that our model outperforms other popular models for this task without sophisticated architecture. We also demonstrate the effectiveness of transfer learning in…
LLaVAC: Fine-tuning LLaVA as a Multimodal Sentiment Classifier
iApp Technology Co., Ltd.（iApp科技有限公司）；Panasonic Research and Development on Artificial Intelligence (AI)（松下人工智能研发中心）；Sirindhorn International Institute of Technology, Thammasat University（泰国法政大学诗琳通国际理工学院）
RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-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, UCAS（中国科学院大学网络空间安全学院）；Academy of Cyber（网络空间研究院）
EFSA: Towards Event-Level Financial Sentiment Analysis
Chinese Academy of Sciences (CAS)（中国科学院）；Institute of Computing Technology, CAS（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）；Henan Institute of Advanced Technology, Zhengzhou University（郑州大学河南先进技术研究院）；School of IoT Engineering, Jiangsu Vocational College of Information Technology（江苏信息职业技术学院物联网工程学院）；Shenzhen Stock Exchange（深圳证券交易所）
University of Zagreb（萨格勒布大学）；Tilde（蒂尔德公司）；University of Latvia（拉脱维亚大学）
In this paper, we present various pre-training strategies that aid in im-proving the accuracy of the sentiment classification task. We, at first, pre-trainlanguage representation models using these strategies and then fine-tune them onthe downstream task. Experimental results on a time-balanced tweet evaluation setshow the improvement over the previous technique. We achieve 76% accuracy forsentiment analysis on Latvian tweets, which is a substantial improvement over pre-vious work
Natural language processing (NLP) has been applied to various fields including text classification and sentiment analysis. In the shared task of sentiment analysis of code-mixed tweets, which is a part of the SemEval-2020 competition~\cite{patwa2020sentimix}, we preprocess datasets by replacing emoji and deleting uncommon characters and so on, and then fine-tune the Bidirectional Encoder Representation from Transformers(BERT) to perform the best. After exhausting top3 submissions, Our team MeisterMorxrc achieves an averaged F1 score of 0.730 in this task, and and our codalab username is Meiste…
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 consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL). Our neural model is trained on document sentiment labels, and learns to predict the sentiment of text segments, i.e. sentences or elementary discourse units (EDUs), without segment-level supervision. We introduce an attention-based polarity scoring method for identifying positive and negative text snippets and a new dataset which we call SPOT (as shorthand for Segment-level POlariTy annotations) for evaluating MIL-style sentiment models like ours. Experimental results demonstrate s…
Exploring Cognitive and Aesthetic Causality for Multimodal Aspect-Based Sentiment Analysis
School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Saw Swee Hock School of Public Health, National University of Singapore（新加坡国立大学 Saw Swee Hock 公共卫生学院）
University College London（伦敦大学学院）；Bloomsbury AI；University of Warwick（华威大学）
In this paper, we introduce the task of targeted aspect-based sentiment analysis. The goal is to extract fine-grained information with respect to entities mentioned in user comments. This work extends both aspect-based sentiment analysis that assumes a single entity per document and targeted sentiment analysis that assumes a single sentiment towards a target entity. In particular, we identify the sentiment towards each aspect of one or more entities. As a testbed for this task, we introduce the SentiHood dataset, extracted from a question answering (QA) platform where urban neighbourhoods are …
We explore the task of sentiment analysis on Hinglish (code-mixed Hindi-English) tweets as participants of Task 9 of the SemEval-2020 competition, known as the SentiMix task. We had two main approaches: 1) applying transfer learning by fine-tuning pre-trained BERT models and 2) training feedforward neural networks on bag-of-words representations. During the evaluation phase of the competition, we obtained an F-score of 71.3% with our best model, which placed $4^{th}$ out of 62 entries in the official system rankings.
Institute of Philosophy and Sociology, Polish Academy of Sciences（波兰科学院哲学与社会学研究所）；University of Warsaw（华沙大学）
We describe a variant of Child-Sum Tree-LSTM deep neural network (Tai et al, 2015) fine-tuned for working with dependency trees and morphologically rich languages using the example of Polish. Fine-tuning included applying a custom regularization technique (zoneout, described by (Krueger et al., 2016), and further adapted for Tree-LSTMs) as well as using pre-trained word embeddings enhanced with sub-word information (Bojanowski et al., 2016). The system was implemented in PyTorch and evaluated on phrase-level sentiment labeling task as part of the PolEval competition.
Controlled text generation techniques aim to regulate specific attributes (e.g. sentiment) while preserving the attribute independent content. The state-of-the-art approaches model the specified attribute as a structured or discrete representation while making the content representation independent of it to achieve a better control. However, disentangling the text representation into separate latent spaces overlooks complex dependencies between content and attribute, leading to generation of poorly constructed and not so meaningful sentences. Moreover, such an approach fails to provide a finer…
M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）
Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA). In this paper, we construct an auxiliary sentence from the aspect and convert ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI). We fine-tune the pre-trained model from BERT and achieve new state-of-the-art results on SentiHood and SemEval-2014 Task 4 datasets.
PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore
School of Information Technology（信息科技学院）；Network Security Research Center（网络安全研究中心）；Big Data and Policing Technology Laboratory（大数据与警务技术实验室）；Guangxi Police College（广西警察学院）
Aspect-Target Sentiment Classification (ATSC) is a subtask of Aspect-Based Sentiment Analysis (ABSA), which has many applications e.g. in e-commerce, where data and insights from reviews can be leveraged to create value for businesses and customers. Recently, deep transfer-learning methods have been applied successfully to a myriad of Natural Language Processing (NLP) tasks, including ATSC. Building on top of the prominent BERT language model, we approach ATSC using a two-step procedure: self-supervised domain-specific BERT language model finetuning, followed by supervised task-specific finetu…
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…
This paper introduces a study on tweet sentiment classification. Our task is to classify a tweet as either positive or negative. We approach the problem in two steps, namely embedding and classifying. Our baseline methods include several combinations of traditional embedding methods and classification algorithms. Furthermore, we explore the current state-of-the-art tweet analysis model, BERTweet, and propose a novel approach in which features are engineered from the hidden states and attention matrices of the model, inspired by empirical study of the tweets. Using a multi-layer perceptron trai…
Carnegie Mellon University（卡内基梅隆大学）；University of California（加利福尼亚大学）
Contrastive learning techniques have been widely used in the field of computer vision as a means of augmenting datasets. In this paper, we extend the use of these contrastive learning embeddings to sentiment analysis tasks and demonstrate that fine-tuning on these embeddings provides an improvement over fine-tuning on BERT-based embeddings to achieve higher benchmarks on the task of sentiment analysis when evaluated on the DynaSent dataset. We also explore how our fine-tuned models perform on cross-domain benchmark datasets. Additionally, we explore upsampling techniques to achieve a more bala…
The University of Melbourne（墨尔本大学）；School of Computing and Information Systems（计算与信息系统学院）
While neural networks have been shown to achieve impressive results for sentence-level sentiment analysis, targeted aspect-based sentiment analysis (TABSA) --- extraction of fine-grained opinion polarity w.r.t. a pre-defined set of aspects --- remains a difficult task. Motivated by recent advances in memory-augmented models for machine reading, we propose a novel architecture, utilising external "memory chains" with a delayed memory update mechanism to track entities. On a TABSA task, the proposed model demonstrates substantial improvements over state-of-the-art approaches, including those usi…
SSP-based construction of evaluation-annotated data for fine-grained aspect-based sentiment analysis
DICORA, Hankuk University of Foreign Studies（DICORA，韩国外国语大学）；DICORA, Hankuk University of LIGM, Université Gustave Eiffel（DICORA，韩国立国民大学，古斯塔夫·埃菲尔大学）；Hankuk University of Foreign Studies（韩国外国语大学）
LLaMA-Based Models for Aspect-Based Sentiment Analysis
University of West Bohemia, Faculty of Applied Sciences（西波西米亚大学应用科学学院）；Department of Computer Science and Engineering（计算机科学与工程系）；NTIS – New Technologies for the Information Society（信息社会新技术机构）
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）
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 …
