Phrase dependency parsing for opinion mining

Wu, Yuanbin; Zhang, Qi; Huang, Xuangjing; Wu, Lide

doi:10.3115/1699648.1699700

Cited by 273 publications

(155 citation statements)

References 17 publications

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“…The authors of [25] used a phrase-based model for dependency parsing, and those of [26] proposed a hybrid approach to phrase-and syntax-based SMTs. However, the tasks in these studies differed from the problems addressed in the present study: the segmentation and tagging problem and Korean morphological analysis.…”

Section: Related Workmentioning

confidence: 99%

Phrase-Based Statistical Model for Korean Morpheme Segmentation and POS Tagging

Kim

2018

IEICE Trans. Inf. & Syst.

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SUMMARY In this paper, we propose a novel phrase-based model for Korean morphological analysis by considering a phrase as the basic processing unit, which generalizes all the other existing processing units. The impetus for using phrases this way is largely motivated by the success of phrase-based statistical machine translation (SMT), which convincingly shows that the larger the processing unit, the better the performance. Experimental results using the SEJONG dataset show that the proposed phrasebased models outperform the morpheme-based models used as baselines. In particular, when combined with the conditional random field (CRF) model, our model leads to statistically significant improvements over the state-of-the-art CRF method.

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Section: Related Workmentioning

confidence: 99%

Phrase-Based Statistical Model for Korean Morpheme Segmentation and POS Tagging

Kim

2018

IEICE Trans. Inf. & Syst.

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“…This forms our Dataset 2 . Table 4: Domain specific accuracy for our rule based system in dataset 2 Now, each sentence in this new dataset has a mixed emotion about various features.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…al [2] use phrase dependency parsing for opinion mining. In dependency grammar, structure is determined by the relation between a head and its dependents.…”

Section: Related Workmentioning

confidence: 99%

Feature Specific Sentiment Analysis for Product Reviews

Mukherjee

Bhattacharyya

2012

Lecture Notes in Computer Science

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Abstract:In this paper, we present a novel approach to identify feature specific expressions of opinion in product reviews with different features and mixed emotions. The objective is realized by identifying a set of potential features in the review and extracting opinion expressions about those features by exploiting their associations. Capitalizing on the view that more closely associated words come together to express an opinion about a certain feature, dependency parsing is used to identify relations between the opinion expressions. The system learns the set of significant relations to be used by dependency parsing and a threshold parameter which allows us to merge closely associated opinion expressions. The data requirement is minimal as this is a one time learning of the domain independent parameters. The associations are represented in the form of a graph which is partitioned to finally retrieve the opinion expression describing the user specified feature. We show that the system achieves a high accuracy across all domains and performs at par with state-of-the-art systems despite its data limitations.

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“…Wu and al. [6] consider the distance in depth between the target candidate and the opinion candidate based on the lowest common parent in the dependencies tree. To improve the performance of these methods, some authors propose hybrid approaches and introduce linguistic rules for weighting the edges in the dependencies tree [30].…”

Section: State Of the Artmentioning

confidence: 99%

“…In literature, some methods analyze the sources of affective states (e.g., who feels the emotional state) [5], and others the targets (e.g., what is the object of the emotional state?) [6]. In this article, we focus on the target, in the specific context of the emotions expressed in health forums.…”

Section: Introductionmentioning

confidence: 99%

Identifying the Targets of the Emotions Expressed in Health Forums

Bringay

Kergosien

Pompidor

et al. 2014

Computational Linguistics and Intelligent Text Processing

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Abstract. In the framework of the French project Patients' Mind, we focus on the semi-automatic analysis of online health forums. Online health forums are areas of exchange where patients, on condition of anonymity, can talk about their personal experiences freely. These resources are a gold mine for health professionals, giving them access to patient to patient exchanges, patient to health professional exchanges and even health professional to health professional exchanges. In this paper, we focus on the emotions expressed by the authors of the messages and more precisely on the targets of these emotions. We suggest an innovative method to identify these targets, based on the notion of semantic roles and using the FrameNet resource. Our method has been successfully validated on real data set.

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Phrase dependency parsing for opinion mining

Cited by 273 publications

References 17 publications

Phrase-Based Statistical Model for Korean Morpheme Segmentation and POS Tagging

Phrase-Based Statistical Model for Korean Morpheme Segmentation and POS Tagging

Feature Specific Sentiment Analysis for Product Reviews

Identifying the Targets of the Emotions Expressed in Health Forums

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