Towards Improving the Lexicon-Based Approach for Arabic Sentiment Analysis

Abdulla, Nawaf; Ahmed, Nizar A.; Shehab, Mohammed A.; Al‐Ayyoub, Mahmoud; Al-Kabi, Mohammed N.; Al-rifai, Saleh

doi:10.4018/978-1-4666-9840-6.ch091

Cited by 11 publications

(31 citation statements)

References 10 publications

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“…Sentiment analysis (SA) and opinion mining (OM) are interchangeable terms [1,2] that were explored by Dave et al [3] and Nasukawa and Yi [4]. According to Ding et al [5], SA can be defined as a quintuple consisting of ( O , F , OO , H and T ), where O is the object, F is a set of features of O , OO is the opinion (opinion orientation) on feature F of object O , H is the opinion holder and T is the time at which the opinion was expressed by H .…”

Section: Introductionmentioning

confidence: 99%

Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis

Assiri

Emam

Al-Dossari

2017

Journal of Information Science

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Sentiment analysis (SA) techniques are applied to assess aspects of language that are used to express feelings, evaluations and opinions in areas such as customer sentiment extraction. Most studies have focused on SA techniques for widely used languages such as English, but less attention has been paid to Arabic, particularly the Saudi dialect. Most Arabic SA studies have built systems using supervised approaches that are domain dependent; hence, they achieve low performance when applied to a new domain different from the learning domain, and they require manually labelled training data, which are usually difficult to obtain. In this article, we propose a novel lexicon-based algorithm for Saudi dialect SA that features domain independence. We created an annotated Saudi dialect dataset and built a large-scale lexicon for the Saudi dialect. Then, we developed our weighted lexicon-based algorithm. The proposed algorithm mines the associations between polarity and non-polarity words for the dataset and then weights these words based on their associations. During algorithm development, we also proposed novel rules for handling some linguistic features such as negation and supplication. Several experiments were performed to evaluate the performance of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis

Assiri

Emam

Al-Dossari

2017

Journal of Information Science

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“…Arabic language is one of the most widely spoken semantic, sharing many commonalities with other semantic languages in terms of vocabularies, vowels, morphologies and word orders [3,4]. Arabic topic classification (ATC) is considered one of the most challenging research topics.…”

Section: Introductionmentioning

confidence: 99%

An Effective Arabic Text Classification Approach Based on Kernel Naive Bayes Classifier

Al-khurayji¹,

Sameh²

2017

IJAIA

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“…The authors compared between the performance of their system on sentence-level and documentlevel. Other lexicon-based works include [12], [7].…”

Section: Background and Related Workmentioning

confidence: 99%

Hierarchical Classifiers for Multi-Way Sentiment Analysis of Arabic Reviews

Al‐Ayyoub¹,

Nuseir²,

Kanaan³

et al. 2016

ijacsa

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Abstract-Sentiment Analysis (SA) is one of hottest fields in data mining (DM) and natural language processing (NLP). The goal of SA is to extract the sentiment conveyed in a certain text based on its content. While most current works focus on the simple problem of determining whether the sentiment is positive or negative, Multi-Way Sentiment Analysis (MWSA) focuses on sentiments conveyed through a rating or scoring system (e.g., a 5-star scoring system). In such scoring systems, the sentiments conveyed in two reviews of close scores (such as 4 stars and 5 stars) can be very similar creating an added challenge compared to traditional SA. One intuitive way of handling this challenge is via a divide-and-conquer approach where the MWSA problem is divided into a set of sub-problems allowing the use of customized classifiers to differentiate between reviews of close scores. A hierarchical classification structure can be used with this approach where each node represents a different classification sub-problem and the decision from it may lead to the invocation of another classifier. In this work, we show how the use of this divide-and-conquer hierarchical structure of classifiers can generate better results than the use of existing flat classifiers for the MWSA problem. We focus on the Arabic language for many reasons such as the importance of this language and the scarcity of prior works and available tools for it. To the best of our knowledge, very few papers have been published on MWSA of Arabic reviews. One notable work is that of Ali and Atiya, in which the authors collected a large scale Arabic Book Reviews (LABR) dataset and made it publicly available. Unfortunately, the baseline experiments on this dataset had very low accuracy. We present two different hierarchical structures and compare their accuracies with the flat structure using different core classifiers. The comparison is based on standard accuracy measures such as precision and recall in addition to using the mean squared error (MSE) as a more accurate measure given the fact that not all misclassifications are the same. The results show that, in general, hierarchical classifiers give significant improvements (of more than 50% in certain cases) over flat classifiers.

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Towards Improving the Lexicon-Based Approach for Arabic Sentiment Analysis

Cited by 11 publications

References 10 publications

Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis

Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis

An Effective Arabic Text Classification Approach Based on Kernel Naive Bayes Classifier

Hierarchical Classifiers for Multi-Way Sentiment Analysis of Arabic Reviews

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