Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks

Schmitt, Martin; Steinheber, Simon; Schreiber, Konrad; Roth, Benjamin

doi:10.18653/v1/d18-1139

Cited by 94 publications

(69 citation statements)

References 9 publications

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“…Other works using neural network models are focused not only on the sentiment polarity of textual content, but also on aspect sentiment analysis [6,11,31,[52][53][54]. Salas-Zárate et al [31] used semantic annotation (diabetes ontology) to identify aspects from which they performed aspect-based sentiment analysis using SentiWordNet.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis Based on Deep Learning: A Comparative Study

2020

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The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users' opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input features.

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Section: Related Workmentioning

confidence: 99%

Sentiment Analysis Based on Deep Learning: A Comparative Study

2020

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“…Compared to this classification problem, the second one and the third one, namely, Aspectoriented Opinion Words Extraction (AOWE) (Fan * The work described in this paper is substantially supported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: 14204418). 1 Our code is open-source and available at: https:// github.com/lixin4ever/BERT- E2E-ABSA et al, 2019) and End-to-End Aspect-based Sentiment Analysis (E2E-ABSA) (Ma et al, 2018a;Schmitt et al, 2018;Li et al, 2019a;Lu, 2017, 2019), are related to a sequence tagging problem. Precisely, the goal of AOWE is to extract the aspect-specific opinion words from the sentence given the aspect.…”

Section: Introductionmentioning

confidence: 99%

Exploiting BERT for End-to-End Aspect-based Sentiment Analysis

Bing

Zhang

et al. 2019

Proceedings of the 5th Workshop on Noisy User-Generated Text (W-Nut 2019)

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In this paper, we investigate the modeling power of contextualized embeddings from pretrained language models, e.g. BERT, on the E2E-ABSA task. Specifically, we build a series of simple yet insightful neural baselines to deal with E2E-ABSA. The experimental results show that even with a simple linear classification layer, our BERT-based architecture can outperform state-of-the-art works. Besides, we also standardize the comparative study by consistently utilizing a hold-out development dataset for model selection, which is largely ignored by previous works. Therefore, our work can serve as a BERT-based benchmark for E2E-ABSA. 1

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“…It enhances the model to learn the knowledge and information shared between various tasks. Among the reviewed studies, several studies [36], [111] applied multi-task learning to ASC in a deep neural framework and achieved some improvements over single task learning. The advantages of adopting deep neural network based multi-task learning can be summarized as follows: 1) learning multiple tasks can avoid overfitting by generating the shared hidden representations; 2) auxiliary task provides interpretable output for explaining the classification; 3) multitask can alleviate the sparsity problem for an implicit data augmentation provided.…”

Section: B Deep Multi-task Learning For Ascmentioning

confidence: 99%

Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision, and Challenges

et al. 2019

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This survey focuses on deep learning-based aspect-level sentiment classification (ASC), which aims to decide the sentiment polarity for an aspect mentioned within the document. Along with the success of applying deep learning in many applications, deep learning-based ASC has attracted a lot of interest from both academia and industry in recent years. However, there still lack a systematic taxonomy of existing approaches and comparison of their performance, which are the gaps that our survey aims to fill. Furthermore, to quantitatively evaluate the performance of various approaches, the standardization of the evaluation methodology and shared datasets is necessary. In this paper, an in-depth overview of the current state-of-the-art deep learning-based methods is given, showing the tremendous progress that has already been made in ASC. In particular, first, a comprehensive review of recent research efforts on deep learningbased ASC is provided. More concretely, we design a taxonomy of deep learning-based ASC and provide a comprehensive summary of the state-of-the-art methods. Then, we collect all benchmark ASC datasets for researchers to study and conduct extensive experiments over five public standard datasets with various commonly used evaluation measures. Finally, we discuss some of the most challenging open problems and point out promising future research directions in this field. INDEX TERMS Aspect based sentiment analysis, aspect-level sentiment classification, attention, convolutional neural network (CNN), deep learning, memory network, neural networks, recurrent neural network (RNN).

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Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks

Cited by 94 publications

References 9 publications

Sentiment Analysis Based on Deep Learning: A Comparative Study

Sentiment Analysis Based on Deep Learning: A Comparative Study

Exploiting BERT for End-to-End Aspect-based Sentiment Analysis

Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision, and Challenges

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