Altruistic Crowdsourcing for Arabic Speech Corpus Annotation

Bougrine, Soumia; Cherroun, Hadda; Abdelali, Ahmed

doi:10.1016/j.procs.2017.10.102

Cited by 7 publications

(6 citation statements)

References 6 publications

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“…We can find several ways to achieve annotation with. Annotation by 2-5 persons having some specified skills (Alotaibi and Anderson, 2016) (Pustejovsky and Stubbs, 2012), Crowdsourcing where the annotation is done by an important number of annotators without specific skills (Bougrine et al, 2017), or Annotation based on rating systems offered by opinion sites (Rushdi-Saleh et al, 2011).…”

Section: Matter Approachmentioning

confidence: 99%

SANA: Sentiment analysis on newspapers comments in Algeria

Rahab

Zitouni

Djoudi

2021

Journal of King Saud University - Computer and Information Scie

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a b s t r a c tIt is very current in today life to seek for tracking the people opinion from their interaction with occurring events. A very common way to do that is comments in articles published in newspapers web sites dealing with contemporary events. Sentiment analysis or opinion mining is an emergent field who's the purpose is finding the behind phenomenon masked in opinionated texts. We are interested in our work by comments in Algerian newspaper websites. For this end, two corpora were used; SANA and OCA. SANA corpus is created by collection of comments from three Algerian newspapers, and annotated by two Algerian Arabic native speakers, while OCA is a freely available corpus for sentiment analysis. For the classification we adopt Supports vector machines, naïve Bayes and k-nearest neighbors. Obtained results are very promising and show the different effects of stemming in such domain, also k-nearest neighbors gives important improvement comparing to other classifiers unlike similar works where SVM is the most dominant. From this study we observe the importance of dedicated resources and methods the newspaper comments sentiment analysis which we look forward in future works. Ó 2019 Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Section: Matter Approachmentioning

confidence: 99%

SANA: Sentiment analysis on newspapers comments in Algeria

Rahab

Zitouni

Djoudi

2021

Journal of King Saud University - Computer and Information Scie

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“…While online crowdsourcing markets make it convenient to pay for workers willing to solve a range of diferent tasks, they suffer from limitations such as not attracting enough workers with desired background or skills [4,21,54]. For example, it can be a challenge to recruit workers for a task that requires workers who speak a specifc language or who live in a certain city [10,42] . Situated crowdsourcing can help fll in the gaps in these scenarios where the crowd needs to be associated with some context.…”

Section: Related Work 21 Situated Crowdsourcingmentioning

confidence: 99%

Section: Community-situated Crowdsourcing: Generic Platforms Versus Targeted Online Communitiesmentioning

confidence: 99%

“…As another example, altruistic crowdsourcing refers to cases where unpaid tasks are carried out by a large number of volunteer contributors [10]. This form of crowdsourcing often utilizes members of the same community to complete collective tasks or getting better quality and more trustworthy information [10,27]. However, some situations require altruistic contribution from outgroup crowd workers, for example, in providing directions to people with visual impairments [11], or in generating valuable daily life advice for people with autism [33].…”

Section: Community-situated Crowdsourcing: Generic Platforms Versus Targeted Online Communitiesmentioning

confidence: 99%

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Comparing Generic and Community-Situated Crowdsourcing for Data Validation in the Context of Recovery from Substance Use Disorders

Rubya

Numainville

Yarosh

2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Targeting the right group of workers for crowdsourcing often achieves better quality results. One unique example of targeted crowdsourcing is seeking community-situated workers whose familiarity with the background and the norms of a particular group can help produce better outcome or accuracy. These communitysituated crowd workers can be recruited in diferent ways from generic online crowdsourcing platforms or from online recovery communities. We evaluate three diferent approaches to recruit generic and community-situated crowd in terms of the time and the cost of recruitment, and the accuracy of task completion. We consider the context of Alcoholics Anonymous (AA), the largest peer support group for recovering alcoholics, and the task of identifying and validating AA meeting information. We discuss the benefts and trade-ofs of recruiting paid vs. unpaid community-situated workers and provide implications for future research in the recovery context and relevant domains of HCI, and for the design of crowdsourcing ICT systems. CCS CONCEPTS• Human-centered computing → Empirical studies in collaborative and social computing.

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“…At document level [32], at sentence level [10] or at word level, also known as Part Of Speech tagging (POS) [22,34]. We can find several ways to achieve annotation with; annotation by 2 to 5 persons having some specified skills [5,29], Crowdsourcing where the annotation is done by an important number of annotators without specific skills [9] or annotation based on rating systems offered by opinion sites [32]. The final version of the annotated data, called the gold standard, is the corpus to be used in the classification step [29].…”

Section: Matter Approachmentioning

confidence: 99%

An Enhanced Corpus for Arabic Newspapers Comments

Rahab

Zitouni

Djoudi

2020

IAJIT

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In this paper, we propose our enhanced approach to create a dedicated corpus for Algerian Arabic newspapers comments. The developed approach has to enhance an existing approach by the enrichment of the available corpus and the inclusion of the annotation step by following the Model Annotate Train Test Evaluate Revise (MATTER) approach. A corpus is created by collecting comments from web sites of three well know Algerian newspapers. Three classifiers, support vector machines, naïve Bayes, and k-nearest neighbors, were used for classification of comments into positive and negative classes. To identify the influence of the stemming in the obtained results, the classification was tested with and without stemming. Obtained results show that stemming does not enhance considerably the classification due to the nature of Algerian comments tied to Algerian Arabic Dialect. The promising results constitute a motivation for us to improve our approach especially in dealing with non Arabic sentences, especially Dialectal and French ones

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Altruistic Crowdsourcing for Arabic Speech Corpus Annotation

Cited by 7 publications

References 6 publications

SANA: Sentiment analysis on newspapers comments in Algeria

SANA: Sentiment analysis on newspapers comments in Algeria

Comparing Generic and Community-Situated Crowdsourcing for Data Validation in the Context of Recovery from Substance Use Disorders

An Enhanced Corpus for Arabic Newspapers Comments

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