# Sentiment Analysis on Algerian Dialect with Transformers
###### Abstract  
The task of extracting sentiment from text has been widely studied in the field of natural language processing. However, little work has been conducted specifically on the Arabic language with the Algerian dialect. In this research, we aim to make a significant contribution to the field of sentiment analysis on the Algerian dialect by creating a custom and relatively large dataset with a tailored deep learning model. The dataset was extracted from Algerian YouTube channels and manually annotated by the research team. We then utilize this dataset to train a state-of-the-art deep learning model for natural language processing called BERT, which is a type of Transformer model. Using this model, we were able to achieve an F1-score of 78.38% and an accuracy of 81.74% on the testing set. This demonstrates the effectiveness of our approach and the potential of using BERT for sentiment analysis on the Algerian dialect. Our model can be used to infer sentiment from any Algerian text, thus providing a valuable tool for understanding the opinions and emotions of the population. This research highlights the importance of studying the Algerian dialect and the potential of using state-of-the-art deep learning models for natural language processing in this area.  
natural language processing; artificial intelligence; deep learning; sentiment analysis; transformers; Algerian dialect 2023200331157 202320031157 202320031157 202320031157 202320031157 202320031157 202320031157 202320031157 202320031157 202320031157 20232003157 20232003157 202320031  
more [14]. By creating a dataset specific to the Algerian dialect, we can improve the performance of the models trained on it and make them more accurate for the dialect.  
Using BERT, we were able to achieve an F1-score of 78.38% and an accuracy of 81.74% on the testing set, demonstrating the effectiveness of our approach and the potential of using BERT for sentiment analysis on the Algerian dialect. This model can be used in various applications such as social media monitoring, customer service, and opinion mining. Furthermore, understanding the sentiment expressed in the Algerian dialect can provide valuable insights into the culture, the people, and their values.  
This research highlights the importance of studying the Algerian dialect and the potential of using state-of-the-art deep learning models for natural language processing in this area. Additionally, our research is a step towards addressing the lack of available data on local dialects and making the most of the opportunities that technology can offer for underdeveloped countries like Algeria. This can be beneficial for businesses and marketers, as it can help them better understand their target audience and improve their products and services. Furthermore, it can be a valuable tool for decision making and policy making.  
Overall, this research provides valuable insights and contributes to the advancement of natural language processing and the understanding of language and culture. It also opens the door for further studies on this topic and can be used as a foundation for future research in this field.  
The structure of the remaining sections in this paper is as follows: The problem statement and research objectives are outlined in Section 2. Section 3 outlines the data collection and analysis process, delving into both our gathered corpus and the pre-existing datasets. A comprehensive exposition of the proposed transformers-based approach is provided in Section 4. The outcomes of our conducted experiments are thoroughly examined and discussed in Section 5. Concluding remarks and prospective directions for future work are encapsulated in Section 6.