Automatic construction of domain-specific sentiment lexicon based on constrained label propagation

Huang, Sheng; Niu, Zhendong; Shi, Chongyang

doi:10.1016/j.knosys.2013.11.009

Cited by 108 publications

(45 citation statements)

References 16 publications

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“…As per their creators [6], sentic computing "relies on the ensemble application of common-sense computing and the psychology of emotions to infer the conceptual and affective information associated with natural language." Other articles worth mentioning explore topics around sentiment lexicon-based techniques, like the contributions of Cho et al [14] and Huang et al [31]. The work by Bravo-Márquez et al [5], on the use of multiple techniques and tools in SA, offers a complete study on how several resources that "are focused on different sentiment scopes" can complement each other.…”

Section: Related Workmentioning

confidence: 99%

A hybrid approach to the sentiment analysis problem at the sentence level

Appel

Chiclana

Carter

et al. 2016

Knowledge-Based Systems

194

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Section: Related Workmentioning

confidence: 99%

A hybrid approach to the sentiment analysis problem at the sentence level

Appel

Chiclana

Carter

et al. 2016

Knowledge-Based Systems

194

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“…Huang [4] utilized the chunk dependency knowledge to extract the domain-specific sentiment lexicon based on constrained label propagation. They had divided the whole strategy into six steps.…”

Section: Related Workmentioning

confidence: 99%

Sentiment lexicon construction using SentiWordNet 3.0

Medagoda

Shanmuganathan

Whalley

2015

2015 11th International Conference on Natural Computation (ICNC)

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Abstract-Opinion mining and sentiment analysis have become popular in linguistic resource rich languages. Opinions for such analysis are drawn from many forms of freely available online/ electronic sources, such as websites, blogs, news re-ports and product reviews. But attention received by less resourced languages is significantly less. This is because the success of any opinion mining algorithm depends on the availability of resources, such as special lexicon and WordNet type tools. In this research, we implemented a less complicated but an effective approach that could be used to classify comments in less resourced languages. We experimented the approach for use with Sinhala Language where no such opinion mining or sentiment analysis has been carried out until this day. Our algorithm gives significantly promising results for analyzing sentiments in Sinhala for the first time.

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“…Sheng Huang et al [13] proposed an automatic construction strategy of domain specific sentiment lexicon based on constrained label propagation. In this method, each steps presented sequentially as follows.…”

Section: Construction Of Constrained Domain-specific Sentiment Lexiconmentioning

confidence: 99%

“…Sheng Huang et al [13] incorporated the contextual and morphological constraints between sentiment terms, and suggested the future works that incorporate more types of constraints knowledge between sentiment terms and to distinguish the aspect-specific polarities. Arturo Montejo-Raez et al [3] stated that how to deal with negation, and to study the context of a specific tweet among the time line of tweets from the particular user in order to identify publisher's mood and adjust final score.…”

Section: Discussed Challenges and Future Developments In Omsamentioning

confidence: 99%