An Aspect Term Extraction Method Based on BiLSTM-CRF with BERT Embedding

A fundamental task of fine-grained sentiment analysis is aspect and opinion terms extraction. Supervised-learning approaches have shown good results for this task; however, they fail to scale across domains where labeled data is lacking. Non pre-trained unsupervised domain adaptation methods that incorporate external linguistic knowledge have proven effective in transferring aspect and opinion knowledge from a labeled source domain to unlabeled target domains; however, pre-trained transformer-based models like BERT and RoBERTa already exhibit substantial syntactic knowledge. In this paper, we propose a method for incorporating external linguistic information into a self-attention mechanism coupled with the BERT model. This enables leveraging the intrinsic knowledge existing within BERT together with externally introduced syntactic information, to bridge the gap across domains. Finally, we demonstrate enhanced results on three benchmark datasets.

Previous studies usually divided aspect-based sentiment analysis into several subtasks in pipeline, i.e., first aspect term and/or opinion term extraction, then aspect-based sentiment prediction, resulting in error propagation and external resources dependency. To overcome the problems mentioned above, in this work we present a novel one-stop solution on aspect-based sentiment analysis. Specifically, we propose a novel multi-task neural learning framework to jointly tackle aspect extraction and sentiment prediction subtasks at the same time, and leverage attention mechanisms to learn the joint representation of aspect-sentiment relationship. We have conducted extensive comparative experiments on two benchmark datasets from SemEva1–2014. The experiment results demonstrate the effectiveness of our proposed solution. Especially, our multi-task model outperforms the state-of-the-art systems on aspect extraction subtask.

sEntIMeldCL: Enhancing explicit knowledge via Uniform-based Implicit Contrastive Mechanism for Aspect-Level Sentiment Analysis.

Aspect term extraction and opinion word extraction are two fundamental subtasks of aspect-based sentiment analysis. The internal relationship between aspect terms and opinion words is typically ignored, and information for the decision-making of buyers and sellers is insufficient. In this paper, we explore an aspect–opinion pair extraction (AOPE) task and propose a Question-Driven Span Labeling (QDSL) model to extract all the aspect–opinion pairs from user-generated reviews. Specifically, we divide the AOPE task into aspect term extraction (ATE) and aspect-specified opinion extraction (ASOE) subtasks; we first extract all the candidate aspect terms and then the corresponding opinion words given the aspect term. Unlike existing approaches that use the BIO-based tagging scheme for extraction, the QDSL model adopts a span-based tagging scheme and builds a question–answer-based machine-reading comprehension task for an effective aspect–opinion pair extraction. Extensive experiments conducted on three tasks (ATE, ASOE, and AOPE) on four benchmark datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches.

In aspect-based sentiment analysis, a fundamental task is extracting aspect terms from opinionated sentences. Aspect term extraction (ATE) has been found to play a critical role among several scenarios, such as service quality improvement and recommendation systems. While deep learning-based methods have achieved great progress in ATE, they mainly consider sequential semantic information and generally ignore the utilisation of syntactic relations of the whole sentence on overall meanings. Furthermore, performances of these methods may also be diminished by poor handling of relation and text noises. To address these issues, we propose a fused sequential and hierarchical representation (FSHR) model, wherein both sequential and hierarchical representations are generated, which facilitates not only the capture of linear semantic information for predicting meaning-related aspect terms but also the utilisation of syntactic relations over the entire sentence to better identify structure-related aspect terms. Moreover, to refine the aspect representation, we incorporate relation-gate mechanism which selectively activates meaningful syntactic dependency paths and design the multi-way aspect attention which prompts the model to focus on relevant text segments about particular aspects. Eventually, sequential and hierarchical representations are adaptively fused for aspect prediction. Experiment results on four datasets demonstrate that FSHR outperforms competitive baselines, and further extensive analyses reveal the effectiveness of our model.

End-to-end aspect-based sentiment analysis (EASA) consists of two sub-tasks: the first extracts the aspect terms in a sentence and the second predicts the sentiment polarities for such terms. For EASA, compared to pipeline and multi-task approaches, joint aspect extraction and sentiment analysis provides a one-step solution to predict both aspect terms and their sentiment polarities through a single decoding process, which avoid the mismatches in between the results of aspect terms and sentiment polarities, as well as error propagation. Previous studies, especially recent ones, for this task focus on using powerful encoders (e.g., Bi-LSTM and BERT) to model contextual information from the input, with limited efforts paid to using advanced neural architectures (such as attentions and graph convolutional networks) or leveraging extra knowledge (such as syntactic information). To extend such efforts, in this paper, we propose directional graph convolutional networks (D-GCN) to jointly perform aspect extraction and sentiment analysis with encoding syntactic information, where dependency among words are integrated in our model to enhance its ability of representing input sentences and help EASA accordingly. Experimental results on three benchmark datasets demonstrate the effectiveness of our approach, where D-GCN achieves state-of-the-art performance on all datasets.

This paper describes our system used in the Aspect Based Sentiment Analysis Task 4 at the SemEval-2014. Our system consists of two components to address two of the subtasks respectively: a Conditional Random Field (CRF) based classifier for Aspect Term Extraction (ATE) and a linear classifier for Aspect Term Polarity Classification (ATP). For the ATE subtask, we implement a variety of lexicon, syntactic and semantic features, as well as cluster features induced from unlabeled data. Our system achieves state-of-the-art performances in ATE, ranking 1st (among 28 submissions) and 2rd (among 27 submissions) for the restaurant and laptop domain respectively.

Fine-grained aspect term extraction is an essential subtask in aspect-based opinion analysis. It aims to identify the aspect terms (also known as opinion targets) of a product or service in each sentence. To learn a good aspect extraction model, an expensive annotation process is usually involved to acquire sufficient token-level labels for each domain, which is not realistic. To address this limitation, some previous works propose domain adaptation strategies to transfer knowledge from a sufficiently labeled source domain to unlabeled target domains. However, due to both the difficulty of fine-grained prediction problems and the large domain gap between different domains, the performance is still far from satisfactory. In this work, we conduct a pioneer study on leveraging sentence-level aspect category labels that can be usually available in commercial services, such as review sites or social media to promote token-level transfer for extraction purpose. Specifically, the aspect category information can be used to construct pivot knowledge for transfer with the assumption that the interactions between the sentence-level aspect category and the token-level aspect terms are invariant across domains. To this end, we propose a novel multilevel reconstruction mechanism that aligns both the fine- and coarse-grained information in multiple levels of abstractions. Comprehensive experiments over several benchmark data sets clearly demonstrate that our approach can fully utilize the sentence-level aspect category labels to improve cross-domain aspect term extraction with a large performance gain.

Aspect-based sentiment analysis (ABSA) has become an essential field in Natural Language Processing (NLP) in recent years.ABSA not only categorizes sentiment as positive, negative, or neutral but also understands the specific aspects or topics discussed in the text review.This study focuses on two important elements of ABSA: Aspect Term Extraction (ATE) and Aspect Sentiment Classification (ASC).This study uses a combination of Sentence Embedding (SBERT) techniques, Part-of-Speech tagging, cosine-similarity calculation to assess words with their respective aspect labels, and the sparse attention mechanism (BIGBIRD) method, which has been proven to increase accuracy effectively and is effective in terms of time and memory usage.By applying this method to two hotel review datasets, Traveloka Review and Semeval 2016 dataset, it is proven to work well on two ABSA tasks, namely ATE and ASC.The results of the ATE test obtained an accuracy of 0.99, and the ASC test obtained an accuracy of 0.89.This study contributes to the advancement of ABSA by introducing a new methodology that improves the accuracy of aspect term extraction and sentiment classification.Additionally, it identifies avenues for future research, including exploring additional techniques to improve model performance and address potential limitations.

With proliferation of user-generated reviews, new opportunities and challenges arise. The advance of Web technologies allows people to access a large amount of reviews of products and services online. Knowing what others like and dislike becomes increasingly important for their decision making in online shopping. The retailers also care more than ever about online reviews, because a vast pool of reviews enables them to monitor reputations and collect feedbacks efficiently. However, people often find difficult times in identifying and summarizing fine-grained sentiments buried in the opinion-rich resources. The traditional sentiment analysis, which focuses on the overall sentiments, fails to uncover the sentiments with regard to the aspects of the reviewed entities. This dissertation studied the research problem of Aspect Based Sentiment Analysis (ABSA), which is to reveal the aspect-dependent sentiment information of review text. ABSA consists of several subtasks: 1) aspect extraction, 2) aspect term extraction, 3) aspect category classification, and 4) sentiment polarity classification at aspect level. We focused on the approach of topic models and neural networks for ABSA. First, to extract the aspects from a collection of reviews and to detect the sentiment polarity regarding the aspects in each review, we proposed a few probabilistic graphical models, which can model words distribution in reviews and aspect ratings at the same time. Second, we presented a multi-task learning model based on long-short term memory and convolutional neural network for aspect category classification and aspect term extraction. Third, for aspect-level sentiment polarity classification, we developed a gated convolution neural network, which can be applied to aspect category sentiment analysis as well as aspect target sentiment analysis.

Aspect-based sentiment analysis (ABSA) aims to detect the sentiment polarities of a sentence with a given aspect. Aspect-term sentiment analysis (ATSA) is a subtask of ABSA, in ATSA, the aspect is given by aspect term: a word or a phrase in sentence. In previous work, most models apply attention mechanism or gating mechanism to capture the key part of the sentence and detect the sentiment polarity by classifying the weighted sum vector, which are regardless of the aspect information during the classification. However, same contexts may show different sentiment polarity with differnet aspects. For example, in sentence “The scene hunky waiters dub diner darling and it sounds like they mean it.” the context “dub diner dearling” shows negative polarity towards the aspect term “waiters”. But in sentence “The diner's husband dub diner darling.”, the same context would show neural polarity towards “husband”. The absent of aspect information would make model get a wrong result in some sentence. To solve this problem, we propose a model called Trasfomer-based context-aspect Relation Detect Model (TRDM), and add two special tokens “[CLSA]” and “[SEPA]” to specify the aspect term in the sentence. TRDM uses the embedding of two tyes of tokens (i.e. “[CLS]” and “[CLSA]”) for classification where “[CLS]” is used to represent the entire sentence in BERT. This scheme enable TRDM to combine the sentence information and aspect information in the step of sentiment polarity classification. We evaluate the performance of our model on two datasets: Restaurant dataset from SemEval2014 and MAMS from NLPCC 2020. Experiment results show that our model obtain noticeable improvement compared with state-of-art transfromer-based models.

Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion term. Recent models perform the triplet extraction in an end-to-end manner but heavily rely on the interactions between each target word and opinion word. Thereby, they cannot perform well on targets and opinions which contain multiple words. Our proposed span-level approach explicitly considers the interaction between the whole spans of targets and opinions when predicting their sentiment relation. Thus, it can make predictions with the semantics of whole spans, ensuring better sentiment consistency. To ease the high computational cost caused by span enumeration, we propose a dual-channel span pruning strategy by incorporating supervision from the Aspect Term Extraction (ATE) and Opinion Term Extraction (OTE) tasks. This strategy not only improves computational efficiency but also distinguishes the opinion and target spans more properly. Our framework simultaneously achieves strong performance for the ASTE as well as ATE and OTE tasks. In particular, our analysis shows that our span-level approach achieves more significant improvements over the baselines on triplets with multi-word targets or opinions.

Sentiment Analysis (SA) is the classification and interpretation of the opinions or emotions within the text data using text analysis methods. Aspect Level Sentiment Analysis (ALSA) is a type of SA technique which uses different types of algorithms to extract the aspects of the entities from the text data and determining the sentiment of the aspects. ALSA is divided into two subtasks such as Aspect Category Detection (ACD) and Aspect Category Polarity Detection (ACPD). ACD determines the aspect categories discussed in each sentence from a set of review sentences and a given predefined set of aspect categories. Aspect categories need not occur as terms in the sentences and are characteristically coarser than the aspect terms. For every review sentence the aspect categories are provided to identify the polarity of aspect category. The objective of ACPD is to identify the polarity such as negative, positive, neutral or conflict of every aspect category mentioned in each sentence. In this paper, the task aspect category polarity detection is addressed. The three types of features such as linguistic features, Lexicons features and Vector Space features are extracted from the reviews. The classifier is trained with these features. The learned model is used to detect the polarities of the categories that are present in the test review sentences. The combination of proposed features with the existing features improves the accuracy of the system. The accuracy of the proposed system has enhanced by 1.3 % compared with best performing system.

Aspect-Based Sentiment Analysis (ABSA) is essential for extracting detailed sentiment polarity regarding specific aspects in product reviews, providing deeper insights into customer opinions on various product attributes. Unlike document-level sentiment analysis, ABSA allows a more granular understanding, crucial for e-commerce analytics and decision-making systems. This study investigates the effectiveness of transformer-based models, such as BERT and RoBERTa, in performing ABSA for product review mining.Purpose:This research aims to explore the application of transformer-based models for aspect-based sentiment analysis, comparing their performance with traditional deep learning models (LSTM and CNN) in the context of mining product reviews. The study evaluates how transformer-based models can more effectively capture sentiment polarity at the aspect level.Methods/Study design/approach:The study uses publicly available product review datasets from large-scale e-commerce platforms, where each review is annotated with aspect terms and sentiment polarities (positive, negative, neutral). The datasets were split into training, validation, and test sets in an 80:10:10 ratio. The models—BERT, RoBERTa, LSTM, and CNN—were fine-tuned on the ABSA task. Performance was evaluated using standard metrics: accuracy, precision, recall, and F1-score.Result/Findings:The results show that transformer-based models, especially RoBERTa, significantly outperform conventional deep learning baselines like LSTM and CNN. RoBERTa achieved the best performance with an accuracy of 0.93 and an F1-score of 0.92, while BERT achieved an accuracy of 0.91 and an F1-score of 0.90. In contrast, LSTM and CNN achieved F1-scores of 0.82 and 0.84, respectively. The transformer models excel in capturing contextual dependencies and associating sentiment polarity with the correct aspects, particularly in complex and multi-aspect sentences. This highlights the superior ability of transformers to handle long-range dependencies and complex sentence structures compared to LSTM and CNN.The findings confirm that transformer-based models are highly effective for aspect-based sentiment analysis, providing a more reliable approach for product review mining. Future research should address the efficiency and interpretability of these models, particularly for large-scale deployment in real-world e-commerce applications.

The aspect term extraction (ATE) task aims to extract aspect terms describing a part or an attribute of a product from review sentences. Most existing works rely on either general or domain embedding to address this problem. Despite the promising results, the importance of general and domain embeddings is still ignored by most methods, resulting in degraded performances. Besides, word embedding is also related to downstream tasks, and how to regularize word embeddings to capture context-aware information is an unresolved problem. To solve these issues, we first propose context-aware dynamic word embedding (CDWE), which could simultaneously consider general meanings, domain-specific meanings, and the context information of words. Based on CDWE, we propose an attention-based convolution neural network, called ADWE-CNN for ATE, which could adaptively capture the previous meanings of words by utilizing an attention mechanism to assign different importance to the respective embeddings. The experimental results show that ADWE-CNN achieves a comparable performance with the state-of-the-art approaches. Various ablation studies have been conducted to explore the benefit of each component. Our code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://github.com/xiejiajia2018/ADWE-CNN</uri> .