Real-Time Sentiment Analysis
Anand Sinha1, Anubhav Bansal1, Himani Gulati1, Ms. Upasna Joshi2
1Student, Department of Computer Science and Engineering, Delhi Technical Campus, Greater Noida, U.P., India
2Professor, Department of Computer Science and Engineering, Delhi Technical Campus, Greater Noida, U.P., India
Abstract
Natural Language Processing (NLP) is a computerized method to text interpretation that is founded on a set of ideas as well as a set of technology. This is a very active field of study and development, there is no universally accepted definition that satisfies everyone, but there are several elements that any intelligent person would consider a person's interpretation.
Sentiment analysis or opinion mining is one of the important aspects of NLP (Natural Language Processing). In recent years, sentiment analysis has received a lot of attention. Sentiment analysis has a wide range of applications, including social media analytics, which simply means creating opinions for individuals on social media by analyzing their sentiments or ideas, which they express through text. It is also an example of how you can review customer feedback and responses. Thus identify the negative comments and reasons why the customers have issues with your product or service. Sentiment analysis enables you to respond to matters promptly before the customer leaves you altogether! In this paper, we aim to tackle the problem of Real-Time sentiment analysis, which is one of the dominant applications of sentiment analysis. Real-time sentiment analysis is an AI-powered solution to analyze the input provided by the user with the help of a pre-trained Machine Learning model. We have used a dataset of 1600000 tweets with 6 different attributes to build our model with the help of machine learning algorithms such as Naïve Bayesian classifier, Neural networks, and VADER. This paper is about the techniques and machine learning algorithms used to analyze the effectiveness of such models.
Keywords: Sentiment Analysis; Machine Learning Algorithms; Naïve Bayesian classifier; Neutral Networks; VADER.
