AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis

Xenos, Dionysios P.; Theodorakakos, Panagiotis; Pavlopoulos, John; Malakasiotis, Prodromos; Androutsopoulos, Ion

doi:10.18653/v1/s16-1050

Cited by 36 publications

(20 citation statements)

References 8 publications

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“…Besides, our Multitask Aspect-based Sentiment Analysis model of classifying the remaining subtasks such as category, aspect and sentiment polarity also achieved remarkable results. [42] 72.34 -CRF [14] 66.54 -AUEB [5] 70.44 -MIN [38] 73.44 -DE-CNN [12] 74.37 -…”

Section: Resultsmentioning

confidence: 99%

Multitask Aspect_Based Sentiment Analysis with Integrated Bidirectional LSTM & CNN Model.

Tran

Thanh

Dang³

et al. 2020

The 4th International Conference on Future Networks and Distributed Systems (ICFNDS)

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Sentiment analysis or opinion mining used to understand the community's opinions on a particular product. Sentiment analysis involves building the opinion collection and classification system. Aspect-based sentiment analysis focuses on the ability to extract and summarize opinions on specific aspects of entities within sentiment document. In this paper, we propose a novel supervised learning approach using deep learning techniques for multitask aspect-based opinion mining system that support four main subtasks: extract opinion target, classify aspect-entity (category), and estimate opinion polarity (positive, neutral, negative) on each extracted aspect of entity. Using extra POS layer to identify morphological features of words combines with stacking architecture of BiLSTM and CNN with word embeddings achieved by training GloVe on Restaurant domain reviews of the SemEval 2016 benchmark dataset in our proposed method is aimed at increasing the accuracy of the model. Experimental results showed that our multitask aspect-based sentiment analysis model has extracted and classified main above subtasks concurrently and achieved significantly better accuracy than the state-of-the-art methods.

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Section: Resultsmentioning

confidence: 99%

Multitask Aspect_Based Sentiment Analysis with Integrated Bidirectional LSTM & CNN Model.

Tran

Thanh

Dang³

et al. 2020

The 4th International Conference on Future Networks and Distributed Systems (ICFNDS)

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“…AUEB was based on CRF sequence labeling model to extract OTEs which were trained on morphological and lexical features as well as word embeddings. An enhancement of 26% was achieved on the restaurant dataset [27].…”

Section: Ote Extraction For the English Languagementioning

confidence: 99%

Extracting Opinion Targets Using Attention-Based Neural Model

2020

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Extracting opinion-target expression is a core subtask to perform aspect-based sentiment analysis which aims to identify the discussed aspects within a text associated with their opinion targets and classify the sentiment as positive, negative, or neutral. This paper proposes a deep learning model to tackle the opinion-target expression extraction task. The proposed model is composed of bidirectional long short-term memory as an encoder, long short-term memory as a decoder with an attention mechanism, and conditional random fields. This model, which operates at the sentence level, is designed to extract opinion targets for the Arabic language. The proposed model's performance is evaluated using SemEval-2016 annotated dataset for the hotels' domain. Experimental results show that the proposed model outperforms the baseline and the prior works, where it achieved an F1 measure of 72.83%.

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“…• DLIREC (Toh and Wang, 2014), AUEB (Xenos et al, 2016): Top-ranked CRF-based systems in ATE subtask in SemEval ABSA challenges (Pontiki et al, 2014(Pontiki et al, , 2016). …”

Section: Experiments Designmentioning

confidence: 99%

Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction

Li¹,

Lam²

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

220

138

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We propose a novel LSTM-based deep multi-task learning framework for aspect term extraction from user review sentences. Two LSTMs equipped with extended memories and neural memory operations are designed for jointly handling the extraction tasks of aspects and opinions via memory interactions. Sentimental sentence constraint is also added for more accurate prediction via another LSTM. Experiment results over two benchmark datasets demonstrate the effectiveness of our framework.

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AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis

Cited by 36 publications

References 8 publications

Multitask Aspect_Based Sentiment Analysis with Integrated Bidirectional LSTM & CNN Model.

Multitask Aspect_Based Sentiment Analysis with Integrated Bidirectional LSTM & CNN Model.

Extracting Opinion Targets Using Attention-Based Neural Model

Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction

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