A Comparative Study of Machine Learning and Deep Learning Techniques for Sentiment Analysis

In this day and age an increasing number of people are using online social networks and services to not only connect and communicate but also to voice their opinions. Sentiment Analysis is the identifying and categorizing of these opinions to determine the public's opinion towards a particular topic, problem, product etc. The importance of Sentiment analysis is increasing day by day. Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Deep Learning is a subfield of machine learning concerned with algorithms that are neural implementations, most commonly seen as neural networks, neural beliefs, etc. It is crucial to employ the most feasible and accurate technique while analyzing sentiments for a given data as this affects both producers as well as consumers. This paper puts forward a study that compares various Machine learning, Deep learning as well as their hybrid techniques. It compares their accuracy for Sentiment Analysis and thus it can be concluded that in most cases Deep learning techniques give better results. However, in some cases the difference in the accuracies of the two techniques is not substantial enough and thus it is better to use Machine Learning methods as they are easier to implement.