A multi-task learning model for Chinese-oriented aspect polarity classification and aspect term extraction

Current TSA evaluation in a cross-domain setup is restricted to the small set of review domains available in existing datasets. Such an evaluation is limited, and may not reflect true performance on sites like Amazon or Yelp that host diverse reviews from many domains. To address this gap, we present YASO -a new TSA evaluation dataset of open-domain user reviews. YASO contains 2215 English sentences from dozens of review domains, annotated with target terms and their sentiment. Our analysis verifies the reliability of these annotations, and explores the characteristics of the collected data. Benchmark results using five contemporary TSA systems show there is ample room for improvement on this challenging new dataset. YASO is available at github.com/IBM/yaso-tsa.

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“…-LCF: 12 (Yang et al, 2020): An end-to-end model based on Song et al (2019), with domain adaptation and a local context focus mechanism.…”

Section: Benchmark Resultsmentioning

confidence: 99%

YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews

Orbach

Toledo-Ronen

Spector

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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“…Fu et al combined a multi-perspective attention framework based on BiLSTM and ASPE joint strategy for simultaneously extracting aspects and opinion pairs, which avoided the error propagation caused by step-by-step extraction (Fu et al, 2021). Yang et al achieved a joint task of extracting aspects and classifying the polarity of aspects with the help of the proposed model LCF-atepc (Yang et al, 2021).…”

Section: Unsupervised Learning Methodsmentioning

confidence: 99%

ALSEE: a framework for attribute-level sentiment element extraction towards product reviews

Zhang

Zhu

et al. 2021

Connection Science

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Attribute-level sentiment element extraction aims to obtain the word pair < opinion target, opinion word > from texts, which mainly obtain fine-grained evaluation information in the attribute level. Due to the information fragmentation and semantic sparseness of product reviews, it is difficult to capture more comprehensive local information from unstructured texts, which leads to the incorrect extraction of some word pairs. Aimed at the problem, this paper proposes a framework for Attribute-Level Sentiment Element Extraction (ALSEE) towards product reviews. Firstly, a small amount of sample data is selected by random sampling, and multiple features (including part of speech, word distance, dependency relationship and semantic role) which are labelled. The labelled data are used as the training set. Then, the Condition Random Field (CRF) model is applied to extract opinion targets (OT) and opinion words (OW). The self-training strategy is used to achieve the semi-supervised learning of CRF model through iterative training. Finally, target-opinion word pairs with modifying relationship are obtained by dependency parsing. Compared with the existing methods, the proposed framework can effectively extract attribute-level sentiment elements though experimental results.

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“…The experiments show that the RoBERTa-based model can outperform or approximate the previous state-of-the-art performances on six datasets across four languages including SemEval 2014 task 4. Recently, authors in [34] proposed a multi-task learning model named LCF-ATEPC for ABSA based on the multi-head selfattention and the local context focus (LCF) [35] mechanisms. The proposed model is multilingual and applicable to the classic English review sentiment analysis task, such as the SemEval-2014 task4.…”

Section: A Aspect-based Sentiment Analysismentioning

confidence: 99%

“…Authors in [34] trained LCF-ATEPC model on commonly used ABSA datasets, including the Laptop and Restaurant datasets of SemEval-2014 Task4, and ACL Twitter social dataset. However, they trained the model on those datasets separately.…”

Section: Aspect-based Sentiment Orientation Scorementioning

confidence: 99%

Cross-Platform Reputation Generation System Based on Aspect-Based Sentiment Analysis

2022

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The active growth of Internet-based applications such as social networks and e-commerce websites leads people to generate a tremendous amount of opinions and reviews about products and services. Thus, it becomes very crucial to automatically process them. Over the last ten years, many systems have been proposed to generate and visualize reputation by mining textual and numerical reviews. However, they have neglected the fact that online reviews could be posted by malicious users that intend to affect the reputation of the target product. Besides, these systems provide an overall reputation value toward the entity and disregard generating reputation scores toward each aspect of the product. Therefore, we developed a system that incorporates spam filtering, review popularity, review posting time, and aspect-based sentiment analysis to generate accurate and reliable reputation values. The proposed model computes numerical reputation values for an entity and its aspects based on opinions collected from various platforms. Our proposed system also offers an advanced visualization tool that displays detailed information about its output. Experiment results conducted on multiple datasets collected from various platforms (Twitter, Facebook, Amazon . . . ) show the efficacy of the proposed system compared with state-of-the-art reputation generation systems.

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A multi-task learning model for Chinese-oriented aspect polarity classification and aspect term extraction

Cited by 83 publications

References 38 publications

YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews

YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews

ALSEE: a framework for attribute-level sentiment element extraction towards product reviews

Cross-Platform Reputation Generation System Based on Aspect-Based Sentiment Analysis

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