MenasrChahir/Sentiment-analysis-of-algerian-dialect

ENHANCING NEGATIVE DISCOURSE DETECTION IN ALGERIAN DIALECT: AN LSTM-RNN BASED NLP APPROACH a supervised machine learning approach using a recurrent neural network (RNN) with Long Short-Term Memory (LSTM) cells to sentiment analysis is proposed. The model is trained on a large and diverse dataset of Algerian dialect called DZSentiA and attempts to distinguish negative and positive discourse. The model demonstrated remarkable performance, particularly in its ability to detect negative discourse which is the main objective of the work, achieving an accuracy rate of 84.7%.