# Sentiment Analysis on Algerian Dialect with Transformers
## 3 Data Collection and Analysis
### Existing Datasets  
Arabic is a semitic language of the Arabs, spoken by more than 400 million people throughout the world, mainly in North Africa and the Middle East [21]. A lot of dialects have emerged over the centuries, including the Maghreb dialect (mainly spoken in Algeria, Tunisia, Libya, Morocco, Western Sahara and Mauritania), the Levantine dialect (spoken in Palestine, Jordan, Syria, and Lebanon), and the Egyptian and Iraqi-gulf dialects [22].  
Among these dialect families, a lot of datasets appeared over the years. Here are few:  
* ASAD [23]: A public dataset extracted from Twitter intended to accelerate research in Arabic NLP in general and Arabic sentiment classification in specific.
* ATSAD [24]: A multi-dialect dataset that was automatically annotated using emojis.
* TSAC [25]: A Tunisian dialect dataset that was extracted from Facebook comments.
* SIACC [26]: Sentiment Polarity Identification on Arabic Algerian Newspaper Comments.
* Ref. [27]: 1000 reviews collected from Algerian press.
* Ref. [28]: Posts and comments collected from Algerian Facebook pages.
* TWIFIL [29]: Annotated tweets collected between 2015 and 2019 were obtained from different geo-locations in Algeria with the help of 26 annotators.
* Ref. [20]: 21,885 posts were collected from Algerian public groups on Facebook.
* SentiALG [30]: An automatically constructed Algerian sentiment corpus containing 8000 messages.  
Table 1 summarizes these datasets along with their size, available classes, annotation approach and dialect. Our dataset is also present in the last line of Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Size** & **Classes** & **Annotation Approach** & **Dialect** \\ \hline ASAD [23] & 100,000 & Negative, Neural and Positive & Manual & Multiple \\ \hline ATSAD [24] & 36,000 & Negative and Positive & From Emojis & Multiple \\ \hline TSAC [25] & 10,000 & Negative and Positive & Manual & Tunisian \\ \hline SIACC [26] & 92 & Negative and Positive & Manual & Algerian \\ \hline [27] & 1000 & / & Manual & Algerian \\ \hline [28] & 5039 & Negative and Positive & Manual & Algerian \\ \hline TWIFIL [29] & 9000 & Negative, Neutral and Positive & Manual & Algerian \\ \hline [20] & 21,885 & Negative-related, Unrelated and Positive & Manual & Algerian \\ \hline SentiALG [30] & 8000 & Negative and Positive & From sentiment lexicons & Algerian \\ \hline Our dataset & 45,000 & Negative, Neutral and Positive & Manual & Algerian \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of related Arabic sentiment analysis datasets.  
A lot of relatively large datasets in multi-dialect Arabic have appeared, and as far as we know, our dataset is among the few Algerian datasets that are sufficient for a deep learning approach in sentiment analysis. Our objective is to develop a model that is versatile and capable of performing sentiment analysis across different domains, thereby providing a more holistic and generalized insight. We believe that focusing on a singular topic, while valuable, could potentially limit the model's adaptability and overall understanding of diverse sentiments, possibly leading to a biased and narrow perspective.