Adaptation of Sentiment Analysis Techniques to Persian Language

Dashtipour, Kia; Hussain, Amir; Gelbukh, Alexander

doi:10.1007/978-3-319-77116-8_10

Cited by 11 publications

(6 citation statements)

References 31 publications

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“…Extensive research in the current literature shows that machine learning has been used in different fields such as sentiment analysis Dashtipour et al (2016a), Dashtipour et al (2016b), Dashtipour et al (2017a), Dashtipour et al (2017b), Gogate et al (2017b), Dashtipour et al (2017c), Shiva et al (2017), Gogate et al (2017a), Gogate et al (2018), Dashtipour et al (2018), Dashtipour et al (2019) 2021), etc., However, research has not been carried out to detect sentiment polarity for tweets related to 5G.…”

Section: Related Workmentioning

confidence: 99%

Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media

et al. 2021

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With the advancement of social media networks, there are lots of unlabeled reviews available online, therefore it is necessarily to develop automatic tools to classify these types of reviews. To utilize these reviews for user perception, there is a need for automated tools that can process online user data. In this paper, a sentiment analysis framework has been proposed to identify people’s perception towards mobile networks. The proposed framework consists of three basic steps: preprocessing, feature selection, and applying different machine learning algorithms. The performance of the framework has taken into account different feature combinations. The simulation results show that the best performance is by integrating unigram, bigram, and trigram features.

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Section: Related Workmentioning

confidence: 99%

Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media

et al. 2021

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“…Incorrect recognition of any of these dots can lead to a misrepresentation of the character and, therefore, the whole word [ 6 , 26 ]. To account for variations in the style, size, and shape of characters in human handwriting [ 11 , 27 , 28 , 29 ], Figure 1 shows some of the characteristics of writing in Arabic, including cursive writing, ligatures, overlapping, diacritics, dots, and loops.…”

Section: Introductionmentioning

confidence: 99%

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Ahmed

Gogate

Tahir

et al. 2021

Entropy

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Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including the high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we have introduced a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we have proposed a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters to prevent overfitting and further enhance its generalization performance when compared to conventional deep learning models. We employed numerous deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The proposed model was extensively evaluated, and it was observed to achieve excellent classification accuracy when compared to the existing state-of-the-art OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). Further comparative experiments were conducted on the respective databases using the pre-trained VGGNet-19 and Mobile-Net models; additionally, generalization capabilities experiments on another language database (i.e., MNIST English Digits) were conducted, which showed the superiority of the proposed DCNN model.

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“…Recently, deep-machine-learning-based automated feature engineering and classification has been shown to outperform state-of-the-art, manual-feature-engineering-based shallow classification. However, both deep and shallow machine learning algorithms have widely been applied to English Corpora, with little work being carried out to develop deep learning models for Persian sentiment analysis [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Dashtipour

Gogate

Adeel

et al. 2021

Entropy

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Sentiment analysis aims to automatically classify the subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning technique to tackle the growing demand for accurate sentiment analysis. However, the majority of research efforts are devoted to English-language only, while information of great importance is also available in other languages. This paper presents a novel, context-aware, deep-learning-driven, Persian sentiment analysis approach. Specifically, the proposed deep-learning-driven automated feature-engineering approach classifies Persian movie reviews as having positive or negative sentiments. Two deep learning algorithms, convolutional neural networks (CNN) and long-short-term memory (LSTM), are applied and compared with our previously proposed manual-feature-engineering-driven, SVM-based approach. Simulation results demonstrate that LSTM obtained a better performance as compared to multilayer perceptron (MLP), autoencoder, support vector machine (SVM), logistic regression and CNN algorithms.

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Adaptation of Sentiment Analysis Techniques to Persian Language

Cited by 11 publications

References 31 publications

Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media

Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Sentiment Analysis of Persian Movie Reviews Using Deep Learning

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