Sampling techniques for Arabic Sentiment Classification

Addi, Hajar Ait; Ezzahir, Redouane

doi:10.1145/3386723.3387899

Cited by 3 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sudanese vocabulary is mostly inspired by MSA, but with important Greek, Turkish and English modifications to the phonology. The morphology of Sudanese words shares many features with MSA, but the method of dialect inflection is Saudi (3C) 90+10 CNN 86.54% [43] Moroccan (2C) 90+10 Majority Voting 83.45% [23] Egyptian, Iraqi and Levantine (3C) 80+10+10 LSTM 71.4 % [6] Jordanian (2C) 90+10 Ensemble 93.4% [5] Lebanon (2C) 80+20 LR 89.80% [1] Algerian (2C) 85+15 SVM 0.86% [31] Tunisian (2C) 80+10+10 Deep-LSTM 90.00% [31] JEG, TAC and TSAC (2C) 90+10 Tw-StAR 82.08% [25] Egyptian, MSA (2C)(n-C) 80+10+10 MC1, MC2 92.96% [7] Modern Standard Arabic 85+15 BiGRU 94.32% [3] Modern Standard Arabic 80+20 RF+SMOTE 96.00% more complicated than MSA in some respects [20]. Following a thorough study of such dialect differences, we have created two datasets based on social media posts, built a CNN-based model for sentiment analyis, and applied it to the datasets.…”

Section: Introductionmentioning

confidence: 99%

A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets

Mustafa¹,

Sutcliffe²,

Sun³

et al. 2022

Preprint

View full text Add to dashboard Cite

Arabic sentiment analysis has become an important research field in recent years. Initially, work focused on Modern Standard Arabic (MSA), which is the most widely-used form. Since then, work has been carried out on several different dialects, including Egyptian, Levantine and Moroccan. Moreover, a number of datasets have been created to support such work. However, up until now, less work has been carried out on Sudanese Arabic, a dialect which has 32 million speakers. In this paper, two new publicly available datasets are introduced, the 2-Class Sudanese Sentiment Dataset (SudSenti2) and the 3-Class Sudanese Sentiment Dataset (SudSenti3). Furthermore, a CNN architecture, SCM, is proposed, comprising five CNN layers together with a novel pooling layer, MMA, to extract the best features. This SCM+MMA model is applied to SudSenti2 and SudSenti3 with accuracies of 92.75% and 84.39%. Next, the model is compared to other deep learning classifiers and shown to be superior on these new datasets. Finally, the proposed model is applied to the existing Saudi Sentiment Dataset and to the MSA Hotel Arabic Review Dataset with accuracies 85.55% and 90.01%.

show abstract

Section: Introductionmentioning

confidence: 99%

A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets

Mustafa¹,

Sutcliffe²,

Sun³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This is because it is evidenced in literature that SVM has great or better performance in different domains such as hotel review (Addi et al 2020;Said & Muqrashi, 2020), education (Guo et al 2020), health (Rasool et al 2020;Adamu et al 2021), movie review (Sharma & Dey, 2012), stock selection (Liu et al 2020) and stock price movement (Sagala et al 2020). SVM has also been shown to perform better than other machine learning models (Bouchlaghem et al 2016;Addi et al 2020) and deep learning models such as CNN, RNN (Alayba et al 2017;Al-Smadi et al 2018), Bi-LSTM (Xing et al 2020) and LSTM (Salehin et al 2020). Xing et al (2020) conducted their study in the financial context, and they showed SVM performed equally well as BERT and performed better than Bi-LSTM.…”

Section: Rq3 Can Machine or Deep Learning Models Perform As An Off-th...mentioning

confidence: 99%

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

View full text Add to dashboard Cite

Banking is an essential component of our day-to-day activity, and the sector contributes to the development of every Nation's economy. To ensure bank stability, the banks have gone through several deregulation and reformation processes. Unfortunately, these processes and technological advancement have led to increased competitiveness, saturated market, and low profitability. Thus, the need for banks to build customer centric service to gain profitability and stability is vital. This opens the need to investigate customers attitude towards banking.With the growing usage of social network sites (SNS), the user generated content (UGC) has opened opportunities for banks to mine customers opinion . This can help the banks to generate insight into their product and services, create marketing strategies and manage their reputation. To the customers, this serves as source to information that can help in supporting decision making on product or service purchase. In this study, sentiment analysis (SA) techniques were employed to investigate customers attitude towards banking us ing Twitter data. However, the unstructured nature of the data and word ambiguity made sentiment analysis complicated. In the context of this study, SA is more difficult because it involves the natural language processing of Pidgin English and English words in the bank domain.Unfortunately, there are limited or no resources for this purpose.

show abstract

Opinion Mining using Sentiment Analysis and Topic Modelling for an Entertainment Event

Samkari

Alsini

2022

2022 Fifth National Conference of Saudi Computers Colleges (NCCC)

View full text Add to dashboard Cite

Sampling techniques for Arabic Sentiment Classification

Cited by 3 publications

References 20 publications

A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets

A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Opinion Mining using Sentiment Analysis and Topic Modelling for an Entertainment Event

Contact Info

Product

Resources

About