SemEval-2014 Task 4: Aspect Based Sentiment Analysis

Pontiki, Maria; Galanis, Dimitrios; Pavlopoulos, John; Papageorgiou, Harris; Androutsopoulos, Ion; Manandhar, Suresh

doi:10.3115/v1/s14-2004

Cited by 1,327 publications

(975 citation statements)

References 21 publications

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“…For both aspect term extraction and aspect category detection, the baseline methodologies are presented in (Pontiki et al, 2014). Table 1 shows the results obtained using our approach as compared to the baseline for aspect term detection, whereas Table 2 outlines the results regarding aspect category detection in terms of the previously mentioned measures.…”

Section: Terms and Category Detectionmentioning

confidence: 99%

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XRCE: Hybrid Classification for Aspect-based Sentiment Analysis

Brun

Popa

Roux

2014

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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In this paper, we present the system we have developed for the SemEval-2014 Task 4 dedicated to Aspect-Based Sentiment Analysis. The system is based on a robust parser that provides information to feed different classifiers with linguistic features dedicated to aspect categories and aspect categories polarity classification. We mainly present the work which has been done on the restaurant domain 1 for the four subtasks, aspect term and category detection and aspect term and category polarity.

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Section: Terms and Category Detectionmentioning

confidence: 99%

“…In order to tackle the Semeval'14 Task 4, (Pontiki et al, 2014), we used our existing aspectbased opinion detection system. The opinion detection system we built relies on a robust deep syntactic parser, (Ait-Mokhtar et al, 2001), as a fundamental component, from which semantic relations of opinion are calculated.…”

Section: Existing Systemmentioning

confidence: 99%

XRCE: Hybrid Classification for Aspect-based Sentiment Analysis

Brun

Popa

Roux

2014

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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“…Since 2014 the SemEval workshop included a shared task on the topic (Pontiki et al, 2014), which has also encouraged the development of new supervised methods. We find approaches based on CRFs such as Mitchell et al (2013) and deep learning (Irsoy and Cardie, 2014) (Liu et al, 2015a), (Zhang et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

Replication issues in syntax-based aspect extraction for opinion mining

Marrese-Taylor¹,

Matsuo²

2017

Proceedings of the Student Research Workshop at the 15th Conference Of the European Chapter of the Association for Co

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Reproducing experiments is an important instrument to validate previous work and build upon existing approaches. It has been tackled numerous times in different areas of science. In this paper, we introduce an empirical replicability study of three well-known algorithms for syntactic centric aspect-based opinion mining. We show that reproducing results continues to be a difficult endeavor, mainly due to the lack of details regarding preprocessing and parameter setting, as well as due to the absence of available implementations that clarify these details. We consider these are important threats to validity of the research on the field, specifically when compared to other problems in NLP where public datasets and code availability are critical validity components. We conclude by encouraging code-based research, which we think has a key role in helping researchers to understand the meaning of the state-of-the-art better and to generate continuous advances.

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“…This work presents a system evaluated in the SemEval Task4: Aspect Based Sentiment Analysis shared task (Pontiki et al, 2014). Our system participated only in subtask 1: Aspect Term Extraction.…”

Section: Introductionmentioning

confidence: 99%

NILC_USP: Aspect Extraction using Semantic Labels

Filho

Pardo

2014

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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This paper details the system NILC USP that participated in the Semeval 2014: Aspect Based Sentiment Analysis task. This system uses a Conditional Random Field (CRF) algorithm for extracting the aspects mentioned in the text. Our work added semantic labels into a basic feature set for measuring the efficiency of those for aspect extraction. We used the semantic roles and the highest verb frame as features for the machine learning. Overall, our results demonstrated that the system could not improve with the use of this semantic information, but its precision was increased.

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SemEval-2014 Task 4: Aspect Based Sentiment Analysis

Cited by 1,327 publications

References 21 publications

XRCE: Hybrid Classification for Aspect-based Sentiment Analysis

XRCE: Hybrid Classification for Aspect-based Sentiment Analysis

Replication issues in syntax-based aspect extraction for opinion mining

NILC_USP: Aspect Extraction using Semantic Labels

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