SemEval-2016 Task 5: Aspect Based Sentiment Analysis

By Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Suresh Manandhar, Ion Androutsopoulos, Núria Bel, Gülşen Eryiğit. International Workshop on Semantic Evaluation, 2016

1,689 citations recorded by the source corpus.

This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for text-level ABSA; the latter was introduced for the first time as a subtask in SemEval. The task attracted 245 submissions from 29 teams.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/dd7bfb6632612e680cc11b4f8570c96ead12942d

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In Artificial Intelligence

1. Bing Liu
2. Hu Xu
3. Zixuan Ke
4. Lei Shu
5. Philip S. Yu
6. Erik Cambria
7. B. Liu
8. Jianfeng Gao

Citations in the corpus

• Structure-Level Knowledge Distillation For Multilingual Sequence Labeling (2020, methodology): “• Aspect Extraction : The dataset is from an aspect-based sentiment analysis task in SemEval-2016 Task 5 (Pontiki et al., 2016).”
• Exploiting BERT for End-to-End Aspect-based Sentiment Analysis (2019, methodology): “The most popular ABSA benchmark datasets are from SemEval ABSA challenges (Pontiki et al., 2014, 2015, 2016) where a few thousand review sentences with gold standard aspect sentiment annotations are provided.”
• Description Based Text Classification with Reinforcement Learning (2020, background): “, 2014; 2015b), aspect sentiment classification (Jo & Oh, 2011; Tang et al., 2015a; Wang et al., 2015; Nguyen & Shirai, 2015; Tang et al., 2016b; Pontiki et al., 2016; Sun et al., 2019b), topic classification (Schwartz et al.”
• A Unified Model for Opinion Target Extraction and Target Sentiment Prediction (2018, background): “The goal of opinion target extraction is to detect the opinion target mentions in the text, and it has been extensively studied (Qiu et al. 2011; Liu, Xu, and Zhao 2013; As observed in some other tasks (Jing et al. 2003; Ng and Low 2004; Finkel and Manning 2009; Miwa and Sasaki 2014), if two…”
• Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, neutral): “& Liu [74] and Semeval-2016, Semeval [172] datasets.”
• Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks (2018, methodology): “Compared to previous methods, our model performs better on the laptop and restaurant datasets from SemEval 2014 [17] 2 Related work”
• Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (2019, background): “Aspect-based sentiment analysis (ABSA) (Jo and Oh, 2011; Pontiki et al., 2014, 2015, 2016) aims to identify fine-grained polarity towards a specific aspect.”
• Sentiment analysis using deep learning architectures: a review (2019, background): “# Instances in training set Baktha and Tripathy (2017) Amazon health product reviews Vanilla RNN LSTM GRU Bi-Vanilla RNN Bi-LSTM Bi-GRU 57.30 78.10 83.90 58.00 79.20 81.10 Amazon: 8000 Xu et al. (2011) Amazon mobile phone reviews Multi-class SVM CRF without interdependencies CRF with interdependenci”
• Position-Aware Tagging for Aspect Sentiment Triplet Extraction (2020, methodology): “Such datasets were all created based on the datasets originally released by SemEval (Pontiki et al., 2014, 2015, 2016).”
• Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis (2019, methodology): “According to the task definition (Pontiki 2014; Pontiki 2015; Pontiki 2016; Li et al. 2018b; Li et al. 2019a), for a term/phrase being regarded as an aspect, it should co-occur with some “opinion terms” that indicate a sentiment polarity on it.”
• Unified Structure Generation for Universal Information Extraction (2022, neutral): “…CoNLL03 (Tjong Kim Sang and De Meul-der, 2003), CoNLL04 (Roth and Yih, 2004), Sci-ERC (Luan et al., 2018), NYT (Riedel et al., 2010), CASIE (Satyapanich et al., 2020), SemEval-14 (Pontiki et al., 2014), SemEval-15 (Pontiki et al., 2015), SemEval-16 (Pontiki et al., 2016), see Table 8 for detail.”
• Target-Dependent Sentiment Classification With BERT (2019, background): “Aspect-Based Sentiment Analysis (ABSA) [12], [19], [24], [28] is a fine-grained task in sentiment analysis, which aims to identify the sentiment polarity (e.”
• Measuring Similarity of Opinion-bearing Sentences (2021, methodology): “Our dataset is based on that of the SemEval 2016 Task 5: “Aspect-Based Sentiment Analysis Subtask 1” (Pontiki et al., 2016).”
• Deep Learning--based Text Classification (2020, background): “Some of the most popular datasets include SemEval-2014 Task 4 [161], Twitter [162], SentiHood [163], to name a few.”