Finding State of Mind Through Emotion and Sentiment Analysis of the Twitter Text

Abstract

In this paper, the main aim of the project is to identify and do analysis of sentiment and emotion of the person and through the analysis find the state of the mind of the person. After finding the state of the mind of the person we can help people through NGO. We know that now top people are using social media twitter, and that place people are posting their thoughts and feelings. In this paper, our job is to do a twitter text analysis and make recommendations based on human emotions and also find state of mind of the person. Here we collect a tweet from the tweeter and their posts and make an analysis of this post. Emotional analysis is the study area for analyzing people’s reviews, emotions, attitudes, and feelings from a tweeter in a written language. Emotional analysis has applications such as data collection and analysis of that data. However, the large volume and unstructured nature of text or data poses a challenge to properly analyzing data. Similarly, skilled algorithms or computer techniques are needed to mine and reduce tweets and find emotional words. Many of the existing computer systems, models, algorithms in sensory diagnostics from such informal data rely on machine learning techniques on the voice bag process as its basis. Understanding public opinion from a tweeter can help improve future decision-making. Comment mines are a way to get knowledge about online services from tweeter blogs, micro blogs, and social media. Individual opinions vary from person to person, and Twitter tweets are the most important source of this type of data. However, the large volume and unstructured nature of text / ideas data poses a challenge to analyzing the efficient data system. we know that millions of people are posting their reviews and comments on Twitter. By performing a tweeter analysis we will use other data science techniques to make an example, processing, classification of Bayes naive, k means algorithm integration, etc.