An experimental study of sentiment classification using deep-based models with various word embedding techniques

Nowadays, sentiment analysis is concerned with identifying and analysing text sentiment. Sentiment analysis has been used in many fields because of its applications in various domains. In the last decade, with the success of machine learning and deep learning methods, many machine- and deep-based sentiment classification have been developed and performed well on various issues. Moreover, word embeddings are important for machine learning and deep learning models since they provide input features in downstream language tasks. This paper presents a comprehensive review of word embeddings and deep learning models. Additionally, we conduct an experimental study of sentiment classification using various deep learning models and word embeddings, in which five deep learning models with four embedding techniques are compared on eight benchmark datasets. In other words, 20 models are evaluated on datasets. Finally, we discuss the performance of models from different perspectives.