Feature-based sentiment analysis in online Arabic reviews

Abdelhamid, Laila; Elzanfaly, Doaa S.; Eldin, Ahmed Sharaf

doi:10.1109/icces.2016.7822011

Cited by 20 publications

(12 citation statements)

References 26 publications

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“…In 2016, the study on MSA was done in lexicon-based using SVM, NB, DT, K-NN and another classifier [52], [53]. Alqasemi et al [52] used the SVM, NB and the k-NN algorithms, which have F1, F2, F3 and F4 features, for carrying out the lexicon-based SA for the Arabic language.…”

Section: Svm Nb Dt and Knnmentioning

confidence: 99%

“…SVM showed a maximal accuracy of 76.46%. Abd-Elhamid et al [53] used a Part Of Speech (POS) tagging feature for carrying out an automatic extraction and weighting of sentiment features from the group of annotated reviews. All the collected features were organized into a tree structure, which highlights the relationship between the objects that are being reviewed and other components.…”

Section: Svm Nb Dt and Knnmentioning

confidence: 99%

“…-Unavailability of dialect Arabic sentiment lexicons and word lists, -Unavailability of a test dataset for Dialect Arabic and modern standard Arabic. Hathlian et al [42], [135], [34], [94], [30], [53], [103], [45], [35], [15], [23], [26], [50] addressed the issues associated with the lack of lexicons, in order to analyse and test the Arabic SA. The researchers collected a large dataset of MSA reviews and comments from social media networks like Twitter and Facebook.…”

Section: Future Research Directionmentioning

confidence: 99%

See 2 more Smart Citations

A Review on Arabic Sentiment Analysis: State-of-the-Art, Taxonomy and Open Research Challenges

Abo¹,

Raj²,

Qazi³

2019

IEEE Access

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Due to the significant use of Arabic language in social media networks, the demand for Arabic sentiment analysis has increased rapidly. Although, numerous sentiment analysis techniques enable people to obtain valuable insights from the opinions shared on social media. However, these techniques are still in their infancy, and the Arabic sentiment analysis domain lacks a compressive survey. Therefore, this study focused on the various characteristics, State-of-the-Art, and the level of sentiment analysis along with the natural language processing applied in the Arabic sentiment analysis. Furthermore, this study also discussed the sentiment analysis of the modern standards and the dialects of Arabic languages along with various machine learning processes and a few popular algorithms. Moreover, this study adds values by critical analysis of two case studies, which displayed an extensive set of the various research communities in this field of sentiment analysis. Finally, open research challenges are investigated, with a focus on the shortage of lexicons; availability; use of Dialect Arabic (DA); lack of corpora and datasets; right to left reading and compound phrases and idioms.

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Section: Svm Nb Dt and Knnmentioning

confidence: 99%

Section: Svm Nb Dt and Knnmentioning

confidence: 99%

Section: Future Research Directionmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Arabic Sentiment Analysis: State-of-the-Art, Taxonomy and Open Research Challenges

Abo¹,

Raj²,

Qazi³

2019

IEEE Access

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“…They have 22000 movie reviews for train set and 3001 movie reviews for test set. While the experiment in [26] used 200 of Arabic user reviews from Forums, Facebook, YouTube, and google search. The researchers stored the reviews in a database tables, then they started data processing by cleaning the text and associated the ratings with the reviews then, they annotated the corpus to organize it into one format (positive, negative, or neutral).…”

Section: Datasets Used In Review Helpfulness Predictionmentioning

confidence: 99%

Review helpfulness prediction: Survey

Almutairi¹,

Abdullah²,

Alahmadi³

2019

PEN

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Online reviews have become the major driving factor influencing purchasing behavior and patterns of social customers. However, it is difficult for customer to cover good reviews about any product or service according to massive amount of reviews latest years. Many previous researches provide innovative models about predicting review helpfulness in E-commerce websites. Some of these studies exploring the direct effect of review attributes on review helpfulness while others focused on reviewer's attributes only. The main objective of this research is to review the most important attributes that have an affect on review helpfulness from many perspectives such as datasets, techniques, frameworks and evaluation methods of the experiments. The paper ends up with important findings about most attributes effect the review helpfulness such as Review Valence.

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“…This kind of summarization is useless in CI because it loses some detailed information, which is important to inform the decision maker of an organization regarding the development and marketing of a product/service. However, summaries based on features provide information about different features and show what customers usually try to search while referring to opinions that rend these types of summaries of great demand in many application domains like: recommender system (Ładyzẏński and Grzegorzewski, 2014 [17]) and trust reputation (Rahimi and Bakkali, 2015 [23]) with different languages such as English (Kansal and Toshniwal, 2014 [15]), Chinese (Zhou et al, 2016 [31]), Arabic (Abd-Elhamid et al, 2016 [1]) and Italian (Maisto and Pelosi, 2014 [21]). However, the large availability of reviews shared in English allows researchers to focus on this language in their works.…”

Section: �� Trod�c�o�mentioning

confidence: 99%

Customer Product Review Summarization over time for Competitive Intelligence

Amarouche¹,

Benbrahim²,

Kassou³

2019

JAMRIS

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Nowadays, Customer's product reviews can be widely found on the Web, be it in personal blogs, forums, or ecommerce websites. They contain important products' in-forma�on and therefore became a new data source for compe��ve intelligence. �n that account, these reviews need to be analyzed and summarized in order to help the leader of an en�ty (company, brand, etc.) to make appropriate decisions in an e�ec�ve way. �owever, most previous review summariza�on studies focus on summarizing sen�ment distribu�on toward di�erent product features without taking into account that the real advantages and disadvantages of a product clarify over �me. For this reason, in this work we aim to propose a new system for product opinion summariza�on which depends on the �me when reviews are e�pressed and that covers the sen�ments change about product features. The proposed system firstly, generates a summary based on product features in order to give more accurate and efficient informa�on about di�erent features. �econdly, classify the product based on its features in its appropriate class (good, medium or bad product) using a fuzzy logic system. The e�perimental results demonstrate the e�ec�veness of the proposed system to generate the real image of a product and its features in reviews.

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Feature-based sentiment analysis in online Arabic reviews

Cited by 20 publications

References 26 publications

A Review on Arabic Sentiment Analysis: State-of-the-Art, Taxonomy and Open Research Challenges

A Review on Arabic Sentiment Analysis: State-of-the-Art, Taxonomy and Open Research Challenges

Review helpfulness prediction: Survey

Customer Product Review Summarization over time for Competitive Intelligence

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