Open AccessSocial Media Behavioral Intelligence using Feature Extraction
Author(s) -
Panchal Mayuriben,
Priyanka Sharma,
Jatin Patel
Publication year - 2021
Publication title -
international journal of scientific research in science, engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-1990
pISSN - 2394-4099
DOI - 10.32628/ijsrset21834
Subject(s) - sentiment analysis , computer science , social media , task (project management) , sentence , product (mathematics) , naive bayes classifier , data science , artificial intelligence , advertising , natural language processing , world wide web , engineering , business , geometry , mathematics , systems engineering , support vector machine
Analysis of the behavioral pattern of a people using data of the social media became a trend in last couple of years. Among this popular network, Twitter, Facebook and the Instagram become more and more popular and that’s why these platforms attract the lots of researchers to predict the sentiment regarding major events like election, product brand, movie, stock market and recent trends are some of them. By identifying the attitude associated with the text in terms of positive, negative or the neutral we are able to analyze the opinion behind the content generated by the user and this opinions about the sentiment are very helpful to for the organization or the political parties or among other entities. The task of sentiment analysis is conducted using identifying the polarity associated with the word or document or we can say sentence. This paper consists research work which is designed to improve the accuracy of the model by improving the Naïve Bayes algorithm and I also worked to improve the 3-gram method during my research