Comprehensive Study on Sentiment Analysis: Insights Into Data Preprocessing, Feature Extraction, and Deep Learning Model Selection

Sentiment analysis, a pivotal task in natural language processing, is influenced by nuanced techniques in data preprocessing, feature extraction, and deep learning model selection. This research comprehensively explores the impact of varied methods, such as preprocessing techniques (steeming and stop-word removal), word embedding models (word2vec, Glove, and FastText), and deep learning architectures (DNN and RNN). Through rigorous evaluations on datasets including IMDB, SST2, and a collection of word tweets, the study identifies successful practices in sentiment classification. The ultimate goal is to offer practical recommendations for practitioners navigating sentiment analysis tasks with deep learning techniques.