Implementation of Sentiment Analysis using Deep Learning

The usage of internet as well as online platforms is booming day by day. Understanding the public opinions can be beneficial for business and political entities in making strategic decisions. In light of this, sentiment analysis plays an important role to understand the polarity of the public opinions. Today, this ocean of data can be used for the fruitful purposes. Analysis of sentiment textual posts can supply knowledge and information that can be used in citizen opinion polling, business intelligence, social contexts, and Internet of Things (IOT)-mood triggered devices. The main focus is the sentiment analysis based on Emotional Recognition (ER). We are going to implement the model for the prediction of sentiment on the basis of general words length words and emoji over any online platform. We are also going to compare the traditional concept related to sentiment analysis. Using the sentiment analysis we can try to control some illegal activities that are post on online platforms such as Movies Reviews Social media etc. In this model we divide the process in the six phases, first is Data overview, second is Data preprocessing third is Feature Engineering fourth is Model selection, fifth is Model Evaluation and last is Model Deployment We can try to take result in two class those are positive and negative or Good and Bad Sentiment. We can try to make two columns first one for data sample and second for the result. Therefore, the above process conclude that the accuracy of the sentiment analysis will be increases by using the length words and emoji from the data.