Sentiment Analysis in Arabic Twitter Posts Using Supervised Methods with Combined Features

Bouchlaghem, Rihab; Elkhelifi, Aymen; Faiz, Rim

doi:10.1007/978-3-319-75487-1_25

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Preprocessing [17] Normalization, POS tagging [24][25][26][27] Stemming [28][29][30][31][32][33] Text cleaning [34][35][36][37][38][39] Normalization, stemming, stop words removal [40][41][42] Text cleaning, normalization, stemming, stop words removal [43][44][45] Normalization Text cleaning, normalization, tokenization, stemming, stop words removal [49][50][51][52] Normalization, tokenization [53,54] Text cleaning, normalization, tokenization [55,56] Normalization, tokenization, POS tagging [13,[57][58][59][60][61][62][63][64] Normalization, tokenization, stemming, stop words removal [65,66] Normalization, tokenization, stemming, lemmatization [67,68] Text cleaning, normalization, tokenization, stemming [69] Text cleaning, tokenization, stemming, negation detection [70]…”

Section: Referencementioning

confidence: 99%

Arabic Sentiment Analysis: A Systematic Literature Review

Ghallab

Mohsen

Ali

2020

Applied Computational Intelligence and Soft Computing

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With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). This paper introduces a systematic review of the existing literature relevant to ASA. The main goals of the review are to support research, to propose further areas for future studies in ASA, and to smoothen the progress of other researchers’ search for related studies. The findings of the review propose a taxonomy for sentiment classification methods. Furthermore, the limitations of existing approaches are highlighted in the preprocessing step, feature generation, and sentiment classification methods. Some likely trends for future research with ASA are suggested in both practical and theoretical aspects.

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Section: Referencementioning

confidence: 99%

Arabic Sentiment Analysis: A Systematic Literature Review

Ghallab

Mohsen

Ali

2020

Applied Computational Intelligence and Soft Computing

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“…These features were used to train several machinelearning algorithms for classification, mainly SVM, Multinomial Naïve Bayes (MNB), Conditional Random Fields (CRF), Decision Trees, and k-Nearest Neighbors (k-NN). Overall, in some cases, SVM achieved better results [21,48,57,58,66,90,91,100,101,110,144,145,147,148,175,194,195,205,206,218,237,307,351,363,365], and in other cases, NB performed better [51,115,117,191,257,286], especially in the case of unbalanced datasets such as in References [306,308,309]. Mostafa [304] claimed that the best classifier is dataset dependent.…”

Section: Feature Engineering "Supervised" Approachesmentioning

confidence: 99%

A Survey of Opinion Mining in Arabic

Badaro

Baly

Hajj

et al. 2019

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Opinion-mining or sentiment analysis continues to gain interest in industry and academics. While there has been significant progress in developing models for sentiment analysis, the field remains an active area of research for many languages across the world, and in particular for the Arabic language, which is the fifth most-spoken language and has become the fourth most-used language on the Internet. With the flurry of research activity in Arabic opinion mining, several researchers have provided surveys to capture advances in the field. While these surveys capture a wealth of important progress in the field, the fast pace of advances in machine learning and natural language processing (NLP) necessitates a continuous need for a more up-to-date literature survey. The aim of this article is to provide a comprehensive literature survey for state-of-the-art advances in Arabic opinion mining. The survey goes beyond surveying previous works that were primarily focused on classification models. Instead, this article provides a comprehensive system perspective by covering advances in different aspects of an opinion-mining system, including advances in NLP software tools, lexical sentiment and corpora resources, classification models, and applications of opinion mining. It also presents future directions for opinion mining in Arabic. The survey also covers latest advances in the field, including deep learning advances in Arabic Opinion Mining. The article provides state-of-the-art information to help new or established researchers in the field as well as industry developers who aim to deploy an operational complete opinion-mining system. Key insights are captured at the end of each section for particular aspects of the opinion-mining system giving the reader a choice of focusing on particular aspects of interest.

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“…The proposed tweet-specific sentiment lexicon [32] is built using a gold seed words set manually annotated and extracted from the data set, and automatically expanded from 500000 tweets using co-occurrence and coordination computing methods. We mainly employed two features inspired from this lexicon:…”

Section: Tweet Specific Lexiconmentioning

confidence: 99%

A Machine Learning Approach For Classifying Sentiments in Arabic tweets

Bouchlaghem¹,

Elkhelifi

Faiz

2016

Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics

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Nowadays, sentiment analysis methods become more and more popular especially with the proliferation of social media platform users number. In the same context, this paper presents a sentiment analysis approach which can faithfully translate the sentimental orientation of Arabic Twitter posts, based on a novel data representation and machine learning techniques. The proposed approach applied a wide range of features: lexical, surface-form, syntactic, etc. We also made use of lexicon features inferred from two Arabic sentiment words lexicons. To build our supervised sentiment analysis system, we use several standard classification methods (Support Vector Machines, K-Nearest Neighbour, Naïve Bayes, Decision Trees, Random Forest) known by their effectiveness over such classification issues.In our study, Support Vector Machines classifier outperforms other supervised algorithms in Arabic Twitter sentiment analysis. Via an ablation experiments, we show the positive impact of lexicon based features on providing higher prediction performance.CCS Concepts • Computing methodologies➝Artificial intelligence➝Natural language processing➝Language resources • Computing methodologies➝Machine learning approaches.

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Sentiment Analysis in Arabic Twitter Posts Using Supervised Methods with Combined Features

Cited by 4 publications

References 13 publications

Arabic Sentiment Analysis: A Systematic Literature Review

Arabic Sentiment Analysis: A Systematic Literature Review

A Survey of Opinion Mining in Arabic

A Machine Learning Approach For Classifying Sentiments in Arabic tweets

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