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This study provides an overview of the application of sentiment analysis (SA) in exploring social movements (SMs). It also compares different models for a SA task of Algerian Arabic tweets related to early days of the Algerian SM, called Hirak.

Related tweets were retrieved using relevant hashtags followed by multiple data cleaning procedures. Foundational machine learning methods such as Naive Bayes, Support Vector Machine, Logistic Regression (LR) and Decision Tree were implemented. For each classifier, two feature extraction techniques were used and compared, namely Bag of Words and Term Frequency–Inverse Document Frequency. Moreover, three fine-tuned pretrained transformers AraBERT and DziriBERT and the multilingual transformer XLM-R were used for the comparison.

The findings of this paper emphasize the vital role social media played during the Hirak. Results revealed that most individuals had a positive attitude toward the Hirak. Moreover, the presented experiments provided important insights into the possible use of both basic machine learning and transfer learning models to analyze SA of Algerian text datasets. When comparing machine learning models with transformers in terms of accuracy, precision, recall and F1-score, the results are fairly similar, with LR outperforming all models with a 68 per cent accuracy rate.

At the time of writing, the Algerian SM was not thoroughly investigated or discussed in the Computer Science literature. This analysis makes a limited but unique contribution to understanding the Algerian Hirak using artificial intelligence. This study proposes what it considers to be a unique basis for comprehending this event with the goal of generating a foundation for future studies by comparing different SA techniques on a low-resource language.

Trading soft commodities has become increasingly challenging with less liquidity in the market, making it very risky and even more costly. Ongoing geopolitical instability, climate change, complex supply chain and fluctuation in demand and supply resulted in a continued price volatility and market uncertainty. Soft commodity trading businesses are under an increasing pressure to adapt to political, economic and social changes. Therefore, this study explores the relationship between brokers and their buyers in the Algerian soft commodities market, with a particular focus on cereals (wheat) products. This study is based on the analysis of secondary data collected from various sources and anecdotal evidences from brokers of soft commodities in Algeria. The overall strategy of Algeria is to limit its reliance on imports. However, political dysfunction coupled with economic instability appears to discourage domestic and foreign investment and inhibit the development of this soft commodities sector. The brokerage firms of soft commodities (wheat, oils, milk powder, rice, coffee, etc.) are operating in a niche market within an environment of intense competition and highly demanding buyers. The striking success of the brokerage function depends on a close relationship formed between the actors (broker, seller and buyer).