Enhancing the determination of aspect categories and their polarities in Arabic reviews using lexicon-based approaches

Obaidat, Islam; Mohawesh, Rami; Al‐Ayyoub, Mahmoud; Al-Smadi, Mohammad; Jararweh, Yaser

doi:10.1109/aeect.2015.7360595

Cited by 44 publications

(13 citation statements)

References 33 publications

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“…The strong lexicon baseline splits the post into sentences or phrases by punctuation, finds the phrase that contains the predicted target, and returns positive if there are more positive words than negative words, and negative otherwise. These baselines are similar to the methods of previously published work for Arabic targeted sentiment Obaidat et al, 2015;Abu-Jbara et al, 2013).…”

Section: Methodsmentioning

confidence: 60%

SMARTies: Sentiment Models for Arabic Target entities

Farra

McKeown

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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We consider entity-level sentiment analysis in Arabic, a morphologically rich language with increasing resources. We present a system that is applied to complex posts written in response to Arabic newspaper articles. Our goal is to identify important entity "targets" within the post along with the polarity expressed about each target. We achieve significant improvements over multiple baselines, demonstrating that the use of specific morphological representations improves the performance of identifying both important targets and their sentiment, and that the use of distributional semantic clusters further boosts performances for these representations, especially when richer linguistic resources are not available.

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Section: Methodsmentioning

confidence: 60%

SMARTies: Sentiment Models for Arabic Target entities

Farra

McKeown

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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“…The recent studies covered a small number of dialects such as Egyptian and Levantine [15] beside the formal language. Also, there are some studies to translate English lexicons to Arabic, but these studies suffer from poor coverage due to the different meaning of translated words [16] [9].…”

Section: A Arabic Language Challengesmentioning

confidence: 99%

Sentiment Analysis for Arabic Social Media

Zayed

Mousa²,

Elmenshawy³

2020

IJCI. International Journal of Computers and Information

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With the rapid spread of social media networks, such as Twitter and Facebook, in Arabic societies, it leads to the explosive growth of Arabic posts, reviews, comments, or tweets. Each one of these generates a huge volume of digital opinionated data on different topics such as politics, economics, societies marketing, and businesses. Analyzing valuable subjective information from opinionated data would assist in a better understanding, making decisions, and predicting global issues and events. Therefore, sentiment analysis coincides with social media networks and has become the most interesting research field in performing the analysis process and detecting sentiment polarities to extracted opinionated social-media data. However, there are several challenges faced the sentiment analysis process, especially with Arabic social data. Sentiment analysis of Arabic social media is indeed in its infantile stage and it has not obtained thoroughly attention wherein several challenging issues still need to address. Some of these challenges result from the complexity of Arabic natural language (e.g., complex morphological and lack of lexicon lists and datasets) and other challenges result from social media platform itself (e.g., slang words and colloquial terms). In this manuscript, we first study the impact of social media challenges on the current challenges of Arabic natural language. Our findings show that such challenges add more complexities to the Arabic sentiment analysis process. Based on these findings, we embark to review the emerged and contributed proposals, which give rise on analyzing opinionated data, extracted from Arabic social media networks. Our review methodology is based on a set of criteria, which we propose to assess and highlight the advantages and limitations of these proposals. The interesting point here is to help researchers identify the social sentiment analysis problems along with a comprehensive survey on the sentiment analysis levels (document, sentence, and aspect levels) and classification approaches (supervised, unsupervised, and hybrid approaches). Finally, we compare these contributed proposals in terms of the average accuracy and suggest a new hybrid approach based on our findings.

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“…While the OCA corpus [33] was used by [13] and [34]. The authors in [28] used the HAAD corpus which was produced by [35]. The HAAD [35] minimized and utilized LABR corpus [36].…”

Section: Corpora and Datasetsmentioning

confidence: 99%