SemEval-2016 Task 5: Aspect Based Sentiment Analysis

Pontiki, Maria; Galanis, Dimitrios; Papageorgiou, Harris; Androutsopoulos, Ion; Manandhar, Suresh; Al-Smadi, Mohammad; Al‐Ayyoub, Mahmoud; Zhao, Yanyan; Qin, Bing; Clercq, Orphée De; Hoste, Véronique; Apidianaki, Marianna; Tannier, Xavier; Loukachevitch, Natalia; Kotelnikov, Evgeniy V.; Bel, Núria; Jiménez-Zafra, Salud María; Eryiğit, Gülşen

doi:10.18653/v1/s16-1002

Cited by 1,093 publications

(758 citation statements)

References 9 publications

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“…Aspect extraction has traditionally been associated with the sentiment analysis community (Liu, 2012;Pontiki et al, 2016), with the goal being to decompose a small document of text (e.g., a review) into multiple facets, each of which may possess their own sentiment marker. For example, a restaurant review may comment on the ambiance, service, and food, preventing the assignment of a uniform sentiment over the entire review.…”

Section: Introductionmentioning

confidence: 99%

Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles

Mitcheltree

Wharton²,

Saluja

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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Reviews of products or services on Internet marketplace websites contain a rich amount of information. Users often wish to survey reviews or review snippets from the perspective of a certain aspect, which has resulted in a large body of work on aspect identification and extraction from such corpora. In this work, we evaluate a newly-proposed neural model for aspect extraction on two practical tasks. The first is to extract canonical sentences of various aspects from reviews, and is judged by human evaluators against alternatives. A kmeans baseline does remarkably well in this setting. The second experiment focuses on the suitability of the recovered aspect distributions to represent users by the reviews they have written. Through a set of review reranking experiments, we find that aspect-based profiles can largely capture notions of user preferences, by showing that divergent users generate markedly different review rankings.

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Section: Introductionmentioning

confidence: 99%

Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles

Mitcheltree

Wharton²,

Saluja

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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“…Therefore, the commonly used word similarity datasets are used to evaluate the proposed semantic similarity method and graphbased IC based on WordNet and DBpedia. Moreover, the semantic similarity methods are evaluated in an aspect category classification task (Pontiki et al, 2015(Pontiki et al, , 2016a in order to evaluate their performance in a real application. This section presents the datasets, implementation, evaluation and provides a brief discussion about the obtained experimental results.…”

Section: Semantic Similarity Evaluationmentioning

confidence: 99%

“…Based on this observation, we proposed a similarity-based framework for concept classification, in which concept's features are represented by frequent collocated words while feature vectors are constructed by computing semantic similarity between input words and feature words. We demonstrate the similarity-based classification framework in the Aspect Based Sentiment Analysis (ABSA) (Pontiki et al, 2015(Pontiki et al, , 2016a task of aspect category classification by proposing both unsupervised model and supervised model.…”

Section: Similarity-based Classificationmentioning

confidence: 99%

“…The English dataset has been provided for two domains: Laptops and Restaurants. We have chosen the English restaurants domain of the ABSA of SemEval2016 (Pontiki et al, 2016b). In the restaurant domain, SemEval predefines a set of entities SERVICE, RESTAURANT, FOOD, DRINKS, AMBIANCE and LOCATION, which can be viewed as general aspect categories.…”

Section: Similarity-based Classificationmentioning

confidence: 99%

“…The N a i = w k ∈f (a i ) count(w k ) denotes the total count of feature words of category a i . The counts of feature words are derived from the annotated datasets from SemEval ABSA (Pontiki et al, 2015(Pontiki et al, , 2016a. We can define…”

Section: Unsupervised Classificationmentioning

confidence: 99%

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SEntFiN 1.0: Entity‐aware sentiment analysis for financial news

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Asso for Info Science & Tech

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Fine-grained financial sentiment analysis on news headlines is a challenging task requiring human-annotated datasets to achieve high performance. Limited studies have tried to address the sentiment extraction task in a setting where multiple entities are present in a news headline. In an effort to further research in this area, we make publicly available SEntFiN 1.0, a humanannotated dataset of 10,753 news headlines with entity-sentiment annotations, of which 2,847 headlines contain multiple entities, often with conflicting sentiments. We augment our dataset with a database of over 1,000 financial entities and their various representations in news media amounting to over 5,000 phrases. We propose a framework that enables the extraction of entity-relevant sentiments using a feature-based approach rather than an expression-based approach. For sentiment extraction, we utilize 12 different learning schemes utilizing lexicon-based and pretrained sentence representations and five classification approaches. Our experiments indicate that lexicon-based N-gram ensembles are above par with pretrained word embedding schemes such as GloVe. Overall, RoBERTa and finBERT (domain-specific BERT) achieve the highest average accuracy of 94.29% and F1-score of 93.27%. Further, using over 210,000 entity-sentiment predictions, we validate the economic effect of sentiments on aggregate market movements over a long duration.

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SemEval-2016 Task 5: Aspect Based Sentiment Analysis

Cited by 1,093 publications

References 9 publications

Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles

Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles

Semantic similarity analysis and application in knowledge graphs

SEntFiN 1.0: Entity‐aware sentiment analysis for financial news

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