Improving Sentiment Analysis in Arabic Using Word Representation

Alayba, Abdulaziz M.; Palade, Vasile; England, Matthew; Iqbal, Rahat

doi:10.1109/asar.2018.8480191

Cited by 63 publications

(38 citation statements)

References 17 publications

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“…This approach has improved the sentiment classification accuracy for our Arabic Health Services (AHS) dataset to reach 0.9424 for the Main-AHS dataset, and 0.9568 for the Sub-AHS dateset, compared to our previous results in [10] which were 0.92 for the Main-AHS dataset and 0.95 for the Sub-AHS dateset. Future work will use some pre-trained word representation models, such as word2vec [16], GloVe [35], and Fasttext [36] for the embedding layer.…”

Section: Discussionmentioning

confidence: 60%

“…We have also obtained good results on using deep neural networks for sentiment analysis on our own dataset, an Arabic Health Services dataset, reported in [9] and [10]. We have obtained an accuracy between 0.85 and 0.91 for the main dataset in [9] using SVM, Naïve Bayes, Logistic Regression and CNNs.…”

Section: Introductionmentioning

confidence: 70%

“…The dataset contains 2026 tweets and it is an unbalanced dataset that has 1398 negative tweets and 628 positive tweets. We call this dataset Main-AHS, and we selected a subset of this dataset, called Sub-AHS, which was introduced in [10]. The Sub-AHS dataset contains 1732 tweets, with 502 positive tweets and 1230 negative tweets.…”

Section: Arabic Health Services Dataset (Main-ahs and Sub-ahs)mentioning

confidence: 99%

“…We have obtained an accuracy between 0.85 and 0.91 for the main dataset in [9] using SVM, Naïve Bayes, Logistic Regression and CNNs. Also, using merged lexicon with CNNs and pre-trained Arabic word embedding, the accuracy for the main dataset was improved to 0.92, and for a Sub-dataset (as described in [10]) the obtained accuracy was between 0.87 and 0.95.…”

Section: Introductionmentioning

confidence: 99%

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A Combined CNN and LSTM Model for Arabic Sentiment Analysis

Alayba

Palade

England

et al. 2018

Lecture Notes in Computer Science

Self Cite

132

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Deep neural networks have shown good data modelling capabilities when dealing with challenging and large datasets from a wide range of application areas. Convolutional Neural Networks (CNNs) offer advantages in selecting good features and Long Short-Term Memory (LSTM) networks have proven good abilities of learning sequential data. Both approaches have been reported to provide improved results in areas such image processing, voice recognition, language translation and other Natural Language Processing (NLP) tasks. Sentiment classification for short text messages from Twitter is a challenging task, and the complexity increases for Arabic language sentiment classification tasks because Arabic is a rich language in morphology. In addition, the availability of accurate pre-processing tools for Arabic is another current limitation, along with limited research available in this area. In this paper, we investigate the benefits of integrating CNNs and LSTMs and report obtained improved accuracy for Arabic sentiment analysis on different datasets. Additionally, we seek to consider the morphological diversity of particular Arabic words by using different sentiment classification levels.

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Section: Discussionmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 70%

Section: Arabic Health Services Dataset (Main-ahs and Sub-ahs)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Combined CNN and LSTM Model for Arabic Sentiment Analysis

Alayba

Palade

England

et al. 2018

Lecture Notes in Computer Science

Self Cite

132

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“…In [39], the authors constructed the word2vec model from a large Arabic corpus, gathered from online Arabic newspapers. This gathering process uses ML and convolutional neural networks techniques.…”

Section: C) Hybrid Learning Approachesmentioning

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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