A review on recent advances in deep learning for sentiment analysis: performances, challenges and limitations
Keywords:
Sentiment Analysis(SA), Text, Deep learning, Emotion Recognition (ER), Classifiers, Neural Network (NN)Abstract
Now days the horizons of social online media keep expanding, the impacts they have on people are huge. For example, many businesses are taking advantage of the input from social media to advertise to specific target market. This is done by detecting and analyzing the sentiment (emotions, feelings, opinions) in social media about any topic or product from the texts. There are numerous machine learning as well as natural language processing methods used to examine public opinions with low time complexity. Deep learning techniques, however, have become widely popular in recent times because of their high efficiency and accuracy. This paper provides a complete overview of the common deep learning frameworks used in sentiment analysis in recent time. We offer a taxonomical study of text representations, learning model, evaluation, metrics and implications of recent advances in deep learning architectures. We also added a special emphasis on deep learning methods; the key findings and limitations of different authors are discussed. This will hopefully help other researchers to do further development of deep learning methods in text processing especially for sentiment analysis. The research also presents the quick summaries of the most popular datasets, lexicons with their related research, performance and main features of the datasets. The aim of this survey is to emphasize the ability to solve text-based sentiment analysis challenges in deep learning architectures with successful achievement for accuracy, speed with context, syntactic and semantic meaning. This review paper analyzes uniquely with the progress and recent advances in sentiment analysis based on existing advanced methods and approach based on deep learning with their findings, performance comparisons and the limitations.
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