An Experimental Study on Sentiment Classification of Algerian Dialect Texts

Moudjari, Leila; Akli-Astouati, Karima

doi:10.1016/j.procs.2020.09.111

Cited by 16 publications

(9 citation statements)

References 16 publications

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“…However, the classification task achieved a good accuracy of 0.816% by BiLSTM models. In the study [64], the researchers conducted a comparison of the modelling outcomes achieved by deep learning models against those of other frequently utilized algorithms. Their experimental findings indicate that deep learning models surpass the baseline performance and exhibit higher accuracy, particularly evident in the case of CNNs.…”

Section: B Word-embeddingmentioning

confidence: 99%

“…ViT5 (1) mDeBERTa (1) network's usual flaws. In another study [64] was inspired to contrast machine learning with deep learning models for emotion and opinion on Algerian text. In the same way, they trace the advances in developing integrated embeddings in DL models.…”

Section: A Non-pretrained Techniquesmentioning

confidence: 99%

“…However, The studies [48] and [108] were found to have used NER preprocessing step. Moreover, there is notable variation in the reporting of preprocessing steps, with certain studies such in a study like [64], [69], [70] failing to document any preprocessing steps. This omission is often attributed to the utilization of online open-source data repositories, where the availability of already generated and preprocessed data obviates the need for explicit preprocessing steps.…”

Section: Transfer Learningmentioning

confidence: 99%

See 2 more Smart Citations

Sentiment Analysis in Low-Resource Settings: A Comprehensive Review of Approaches, Languages, and Data Sources

Aliyu,

Sarlan,

Usman Danyaro

et al. 2024

IEEE Access

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The field of low-resource sentiment analysis has seen significant developments in recent years. This research review SLR evaluates the approaches and data sources utilized in low-resource sentiment analysis by deep learning. The primary aim is to discover suitable approaches for future sentiment analysis in low-resource. Our studies explore various languages, models, and data sources expressing a desire to create effective approaches. Our emphasis lies in the critical evaluation of the approaches and the datasets utilized, to identify areas where further research is needed. Our analysis study adds to the existing body of literature reviews, encompassing multilingual low-resource sentiment analysis research spanning from 2018 to 2023. The findings indicate that the transfer learning approach is the most frequently used, followed by word embedding learning and machine translation systems. Additionally, the study shows that social media is the most used platform for data collection, followed by product reviews, movies, and hotels. There has been a significant surge in the adoption of pre-trained transformers, indicating a growing interest in exploring the potential of these models for low-resource languages within the natural language processing (NLP) community. This trend is largely attributed to the novel nature of these models and their feature of being nonlabour intensive. However, the scarcity of annotated datasets for such languages remains a major hurdle. finally, these research findings are relevant and informative for any researcher working in the field of lowresource multilingual sentiment analysis. The study introduces a conceptual framework for performing sentiment analysis in low-resource. The study provides a valuable resource for future researchers.

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Section: B Word-embeddingmentioning

confidence: 99%

Section: A Non-pretrained Techniquesmentioning

confidence: 99%

Section: Transfer Learningmentioning

confidence: 99%

See 1 more Smart Citation

Sentiment Analysis in Low-Resource Settings: A Comprehensive Review of Approaches, Languages, and Data Sources

Aliyu,

Sarlan,

Usman Danyaro

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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“…The author in [44] compared the results of accurate deep learning models with classic models CNN, LSTM, SVM, and NB. They used two datasets: posts and comments collected from Algerian Facebook pages, and the second was the corpus of Algerian labelled tweets.…”

Section: Saudi Dialect For Arabic Review Sentiment Classificationmentioning

confidence: 99%

Tourist Reviews Sentiment Classification using Deep Learning Techniques: A Case Study in Saudi Arabia

Alharbi,

Mezher,

Barakeh

2022

IJACSA

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Now-a-days, social media sites and travel blogs have become one of the most vital expression sources. Tourists express everything related to their experiences, reviews, and opinions about the place they visited. Moreover, the sentiment classification of tourist reviews on social media sites plays an increasingly important role in tourism growth and development. Accordingly, these reviews are valuable for both new tourists and officials to understand their needs and improve their services based on the assessment of tourists. The tourism industry anywhere also relies heavily on the opinions of former tourists. However, most tourists write their reviews in their local dialect, making sentiment classification more difficult because there are no specific rules to control the writing system. Moreover, there is a gap between Modern Standard Arabic (MSA) and local dialects. one of the most prominent issues in sentiment analysis is that the local dialect lexicon has not seen significant development. Although a few lexicons are available to the public, they are sparse and small. Thus, this paper aims to build a model capable of accurate sentiment classification in the Saudi dialect for Arabic in tourist place reviews using deep learning techniques. Machine learning techniques help classifying these reviews into (positive, negative, and neutral). In this paper, three machine learning algorithms were used, Support -Vector Machine (SVM), Long short-term memory (LSTM), and Recurrent Neural Network (RNN). These algorithms are classified using Google Map data set for tourist places in Saudi Arabia. Performance classification of these algorithms is done using various performance measures such as accuracy, precision, recall and Fscore. The results show that the SVM algorithm outperforms the deep learning techniques. The result of SVM was 98%, outperforming the LSTM, and RNN had the same performance of 96%.

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“…For organizations and enterprises, extracting valuable information from consumers is very important. Social media platforms such as Facebook and YouTube collect vast amounts of user reviews that form a rich source of information for companies to understand their customers (Moudjari et al, 2020). The information provided through user reviews is used to classify sentiments essential to the decision-making process of enterprises and organizations (Li et al 2017).…”

Section: Background Of the Studymentioning

confidence: 99%

Tagalog Sentiment Analysis Using Deep Learning Approach With Backward Slang Inclusion

Boquiren,

Garcia,

Hungria

et al. 2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

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As the internet users in the Philippines have become increasingly rampant, Tagalog Sentiment Analysis is essential when determining general sentiments correlated to a topic. Among Tagalog tweets, backward slang has gained increased popularity on social media. Deep learning algorithms such as Long-Short Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) have been proven more effective than traditional machine learning algorithms. This study explored and evaluated the effects of backward slang on two deep learning models using performance metrics for classification models. Results show that backward slang helps in the expressivity of textual data, given the improvement in overall sentiment scores.

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An Experimental Study on Sentiment Classification of Algerian Dialect Texts

Cited by 16 publications

References 16 publications

Sentiment Analysis in Low-Resource Settings: A Comprehensive Review of Approaches, Languages, and Data Sources

Sentiment Analysis in Low-Resource Settings: A Comprehensive Review of Approaches, Languages, and Data Sources

Tourist Reviews Sentiment Classification using Deep Learning Techniques: A Case Study in Saudi Arabia

Tagalog Sentiment Analysis Using Deep Learning Approach With Backward Slang Inclusion

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