Activity meets Sentiment – Analysis Using Deep Learning Frameworks


  • P. Anjaiah, Dr. B.V. Ramesh Naresh Yadav


Sentiment Analysis, Movie Rating system, CNN, Random Forest


Currently, many people perform more research in the online platform to take a perfect decision before initiating any activity to rescue their money, time, and attempt. This research is one of the strive to demonstrate the investigstion of sentiment analysis. The Significance of sentiment analysis is to discover a better opinion in a small time for all the big capacity of the data. Many people are clever and well educated when their resolutions are successful against their conduct. Everybody’s activity in this research is managed from their idea or their concepts by their incidents or their others incidents. Some of the activities are performed by governments, business concerns, and many individuals which are regulated by their thinking. Development of information communication automation utilized by the whole world that molds all the feasible to portion the beliefs, sentiments, affections, and proposals straight away via online platform solicitations. To make a perfect decision many people utilizes social media like Telegram, Facebook, and many more applications which have embellished a powerful mode of educating based on the user opinion and also make use of some of the web blogs which are utilized by many of the individuals and companies to realize the customer belief to make a perfect conclusion. Recently, much of the research prompt researchers in the Sentiment Analysis (SA) concept to attain more accuracy and resolve all the challenges. Currently, opinions and sentiment are interconnected topics, and researchers utilized them correspondently in the research analysis. This research evaluates some of the brand new studies that are been professional in deep learning to resolve SA issues. Here movie review dataset IMDb was analyzed, based on sentiment extracted the movie review. Here Random Forest and CNN algorithms are used to predict the sentiment. 85 % accuracy achieved with CNN model.