Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction

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   From Overall Sentiment to Aspect-Level Insights: A Pretraining Strategy for Unsupervised Aspect-based Sentiment Analysis

   Simone Prete, Giovanni Maria Biancofiore, Fedelucio Narducci et al. · 2025 · User Modeling, Adaptation, and Personalization · cited 1 time · Not summarized yet

   Abstract

   Aspect-Based Sentiment Analysis (ABSA) aims to identify sentiments associated with specific aspects within a text. It plays a crucial role in applications such as product reviews and customer feedback analysis, where understanding nuanced opinions is essential. However, progress in ABSA remains constrained by the need for fine-grained labeled data, limiting the applicability of supervised models in real-world scenarios. In this study, we propose an unsupervised transformer-based approach that leverages sentence-level sentiment annotations to induce aspect-level sentiment representations. By supervising attention distributions during pretraining, our model learns to aggregate token-level sent…

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   Aspect-level sentiment classification with fused local and global context

   Ao Feng, Jia-Zhi Cai, Zhengjie Gao et al. · 2023 · Journal of Big Data · cited 4 times · Not summarized yet

   Abstract

   Sentiment analysis aims to determine the sentiment orientation of a text piece (sentence or document), but many practical applications require more in-depth analysis, which makes finer-grained sentiment classification the ideal solution. Aspect-level Sentiment Classification (ALSC) is a task that identifies the emotional polarity for aspect terms in a sentence. As the mainstream Transformer framework in sentiment classification, BERT-based models apply self-attention mechanism that extracts global semantic information for a given aspect, while a certain proportion of local information is missing in the process. Although recent ALSC models have achieved good performance, they suffer from robu…

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   Aspect sentiment learning for Aspect-Level Sentiment Classification

   Zhong-Quan Jian, Jiajian Li, Meihong Wang et al. · 2025 · Neural Networks · cited 1 time · Not summarized yet

   Abstract

   Aspect-Level Sentiment Classification (ALSC) is a fine-grained Sentiment Analysis (SA) task that aims to determine the sentiments of a sentence toward different aspects. Despite their significant success, most existing methods derive aspect sentiment semantics from individual sentences, overlooking the interrelationships among relevant sentences that could provide a more comprehensive understanding of aspect sentiment semantics. To this end, we propose AspLearn, an aspect-learning method to optimize aspect sentiment semantics and generate more robust aspect-specific sentence features for the ALSC task. In a nutshell, AspLearn employs the Aspect-aware Contrastive Learning (AspCL) to mine valu…

4. Very close Cites this paper Not relevant

   Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis

   Jia-Hao Cao, Rui Liu, Huai-Liang Peng et al. · 2022 · North American Chapter of the Association for Computational Linguistics · cited 14 times · Not summarized yet

   Abstract

   Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task. Most recent efforts adopt pre-trained model to classify the sentences with aspects. However, the aspect sentiment bias from pre-trained model brings some noise to the ABSA task. Besides, traditional methods using cross-entropy loss are hard to find the potential associations between sentiment polarities. In this work, we analyze the ABSA task from a novel cognition perspective: humans can often judge the sentiment of an aspect even if they do not know what the aspect is. Moreover, it is easier to distinguish positive and negative sentiments than others for human beings because positive and negative are tw…

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   Improving span-based Aspect Sentiment Triplet Extraction with part-of-speech filtering and contrastive learning

   Qingling Li, Wu-Shao Wen, Jing-Hui Qin · 2024 · Neural Networks · cited 20 times · Not summarized yet

   Abstract

   Aspect Sentiment Triple Extraction (ASTE), a subtask of fine-grained sentiment analysis, aims to extract aspect terms, opinion terms, and their corresponding sentiment polarities from sentences. Previous methods often enumerated all possible spans of aspects and opinions as candidate spans that contain many invalid and irrelevant spans. This made the model training and prediction more difficult due to noised spans, leading to poor performance. To address this issue, we first propose a novel span-level approach that explicitly considers prior grammatical knowledge to generate possible candidate spans by part-of-speech filtering. In this way, our approach can make the model easier to be traine…

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   Complementary Learning of Aspect Terms for Aspect-based Sentiment Analysis

   Han Qin, Yuanhe Tian, Fei Xia et al. · 2022 · International Conference on Language Resources and Evaluation · cited 3 times · Not summarized yet

   Abstract

   Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity towards a given aspect term in a sentence on the fine-grained level, which usually requires a good understanding of contextual information, especially appropriately distinguishing of a given aspect and its contexts, to achieve good performance. However, most existing ABSA models pay limited attention to the modeling of the given aspect terms and thus result in inferior results when a sentence contains multiple aspect terms with contradictory sentiment polarities. In this paper, we propose to improve ABSA by complementary learning of aspect terms, which serves as a supportive auxiliary task to enhance ABSA by explic…

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   Exploiting Position Bias for Robust Aspect Sentiment Classification

   Fangjie Ma, Chen Zhang, Da-Wei Song · 2021 · Findings · cited 26 times · Not summarized yet

   Abstract

   Aspect sentiment classification (ASC) aims at determining sentiments expressed towards different aspects in a sentence. While state-of-the-art ASC models have achieved remarkable performance, they are recently shown to suffer from the issue of robustness. Particularly in two common scenarios: when domains of test and training data are different (out-of-domain scenario) or test data is adversarially perturbed (adversarial scenario), ASC models may attend to irrelevant words and neglect opinion expressions that truly describe diverse aspects. To tackle the challenge, in this paper, we hypothesize that position bias (i.e., the words closer to a concerning aspect would carry a higher degree of i…

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   Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning

   Bin Liang, Wangda Luo, Xiang Li et al. · 2021 · International Conference on Information and Knowledge Management · cited 53 times · Not summarized yet

   Abstract

   Most existing aspect-based sentiment analysis (ABSA) research efforts are devoted to extracting the aspect-dependent sentiment features from the sentence towards the given aspect. However, it is observed that about 60% of the testing aspects in commonly used public datasets are unknown to the training set. That is, some sentiment features carry the same polarity regardless of the aspects they are associated with (aspect-invariant sentiment), which props up the high accuracy of existing ABSA models when inevitably inferring sentiment polarities for those unknown testing aspects. Therefore, in this paper, we revisit ABSA from a novel perspective by deploying a novel supervised contrastive lear…

Prior work it builds on

1. Close A method it uses Not relevant

   A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis

   Yue Mao, Yi Shen, Chao Yu et al. · 2021 · AAAI Conference on Artificial Intelligence · cited 259 times · Not summarized yet

   Abstract

   Aspect based sentiment analysis (ABSA) involves three fundamental subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Early works only focused on solving one of these subtasks individually. Some recent work focused on solving a combination of two subtasks, e.g., extracting aspect terms along with sentiment polarities or extracting the aspect and opinion terms pair-wisely. More recently, the triple extraction task has been proposed, i.e., extracting the (aspect term, opinion term, sentiment polarity) triples from a sentence. However, previous approaches fail to solve all subtasks in a unified end-to-end framework. In this paper, we propose a c…

2. Close Used in depth by this paper A method it uses Not relevant

   Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree

   Kai Sun, Richong Zhang, Samuel Mensah et al. · 2019 · Conference on Empirical Methods in Natural Language Processing · cited 391 times · Not summarized yet

   Abstract

   We propose a method based on neural networks to identify the sentiment polarity of opinion words expressed on a specific aspect of a sentence. Although a large majority of works typically focus on leveraging the expressive power of neural networks in handling this task, we explore the possibility of integrating dependency trees with neural networks for representation learning. To this end, we present a convolution over a dependency tree (CDT) model which exploits a Bi-directional Long Short Term Memory (Bi-LSTM) to learn representations for features of a sentence, and further enhance the embeddings with a graph convolutional network (GCN) which operates directly on the dependency tree of the…

3. Close Used in depth by this paper A method it uses Not relevant

   Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa

   Junqi Dai, Hang Yan, Tianxiang Sun et al. · 2021 · North American Chapter of the Association for Computational Linguistics · cited 201 times · Not summarized yet

   Abstract

   Aspect-based Sentiment Analysis (ABSA), aiming at predicting the polarities for aspects, is a fine-grained task in the field of sentiment analysis. Previous work showed syntactic information, e.g. dependency trees, can effectively improve the ABSA performance. Recently, pre-trained models (PTMs) also have shown their effectiveness on ABSA. Therefore, the question naturally arises whether PTMs contain sufficient syntactic information for ABSA so that we can obtain a good ABSA model only based on PTMs. In this paper, we firstly compare the induced trees from PTMs and the dependency parsing trees on several popular models for the ABSA task, showing that the induced tree from fine-tuned RoBERTa …

4. Close A method it uses Not relevant

   Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification

   Ming-Hao Hu, Yuxing Peng, Zhen Huang et al. · 2019 · Annual Meeting of the Association for Computational Linguistics · cited 234 times · Not summarized yet

   Abstract

   Open-domain targeted sentiment analysis aims to detect opinion targets along with their sentiment polarities from a sentence. Prior work typically formulates this task as a sequence tagging problem. However, such formulation suffers from problems such as huge search space and sentiment inconsistency. To address these problems, we propose a span-based extract-then-classify framework, where multiple opinion targets are directly extracted from the sentence under the supervision of target span boundaries, and corresponding polarities are then classified using their span representations. We further investigate three approaches under this framework, namely the pipeline, joint, and collapsed models…

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   Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence

   Chi Sun, Luyao Huang, Xipeng Qiu · 2019 · North American Chapter of the Association for Computational Linguistics · cited 762 times · Not summarized yet

   Abstract

   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. The source codes are available at https://github.com/HSLCY/ABSA-BERT-pair.

Papers that cite it

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   Modeling Aspect Sentiment Coherency via Local Sentiment Aggregation

   Heng Yang, Ke Li · 2021 · Findings · cited 26 times · Not summarized yet

   Abstract

   Aspect sentiment coherency is an intriguing yet underexplored topic in the field of aspect-based sentiment classification. This concept reflects the common pattern where adjacent aspects often share similar sentiments. Despite its prevalence, current studies have not fully recognized the potential of modeling aspect sentiment coherency, including its implications in adversarial defense. To model aspect sentiment coherency, we propose a novel local sentiment aggregation (LSA) paradigm based on constructing a differential-weighted sentiment aggregation window. We have rigorously evaluated our model through experiments, and the results affirm the proficiency of LSA in terms of aspect coherency …

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   A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

   Wenxuan Zhang, Xin Li, Yang Deng et al. · 2022 · IEEE Transactions on Knowledge and Data Engineering · cited 485 times · Not summarized yet

   Abstract

   As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle ABSA in different scenarios, various tasks are introduced for analyzing different sentiment elements and their relations, including the aspect term, aspect category, opinion term, and sentiment polarity. Unlike early ABSA works focusing on a single sentiment element, many compound ABSA tasks involving multiple elements have been studied in recent years for capturing more complete aspect-level sentiment information. However, a systematic review of various …

3. Close Uses this paper in depth Not relevant

   Exploiting Careful Design of SVM Solution for Aspect-term Sentiment Analysis

   Han-Feng Liu, Minping Chen, Zhen-Yao Zheng et al. · 2024 · Conference on Empirical Methods in Natural Language Processing · cited 3 times · Not summarized yet

   Abstract

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   Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews

   D. Jayakody, A. Malkith, Koshila Isuranda et al. · 2024 · arXiv.org · preprint, not peer reviewed · cited 14 times · Not summarized yet

   Abstract

   Aspect-based Sentiment Analysis (ABSA) is a critical task in Natural Language Processing (NLP) that focuses on extracting sentiments related to specific aspects within a text, offering deep insights into customer opinions. Traditional sentiment analysis methods, while useful for determining overall sentiment, often miss the implicit opinions about particular product or service features. This paper presents a comprehensive review of the evolution of ABSA methodologies, from lexicon-based approaches to machine learning and deep learning techniques. We emphasize the recent advancements in Transformer-based models, particularly Bidirectional Encoder Representations from Transformers (BERT) and i…

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   A Weak Supervision Approach for Few-Shot Aspect Based Sentiment Analysis

   Robert Vacareanu, Siddharth Varia, Kishaloy Halder et al. · 2023 · Conference of the European Chapter of the Association for Computational Linguistics · cited 6 times · Not summarized yet

   Abstract

   We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks. We propose a pipeline approach to construct a noisy ABSA dataset, and we use it to adapt a pre-trained sequence-to-sequence model to the ABSA tasks. We test the resulting model on three widely used ABSA datasets, before and after fine-tuning. Our proposed method preserves the full fine-tuning performance while showing significant improvements (15.84 absolute F1) in the few-shot learning scenario for the harder tasks. In zero-shot (i.e., without fine-tuning), our method outperforms the previous state of the art on the aspect extraction se…