Sentimental Classification on Social Media Text Using NLP Techniques | Original Article

ABSTRACT:

ABSTRACT

The idea of ​​sentiment analysis has been receiving attention for the past few years. Major challenges in sentiment analysis are collection of huge data from sources, applying appropriate algorithms or techniques and splitting them into different sentiments. In this fast-expanding internet world, social media gives individuals a platform to express their views. In which we can say something. With the changing ways of things in different areas in our daily life, a person can easily express his opinion or idea. This idea does not say under any pressure but expresses itself. People tend to express themselves in their regional language or in a way convenient to them. Here social media has been created to play an important role in the personal feelings of the people. A huge amount of data gets accumulated in the network application, this data is the views and opinions of the person, based on their feelings, the best answer can be read. In this research paper, we will get the information about tweets posted by the customer to be positive, negative or neutral, for this the proposed Twitter model will first create a database of tweets from Twitter by using Twitter API, then we will write Using Text Blob, individual sentiment scores are assigned to the user's goals and using the text classification model they are classified as positive, negative or neutral.