Social media platforms have become an integral part of modern communication, offering individuals an unprecedented opportunity to express their thoughts, opinions, and emotions publicly. The primary objective of social media trends sentiment analysis is to analyze and interpret the sentiments associated with specific hashtags, topics, or viral content through the application of natural language processing and machine learning algorithms. To analyze the trends, we have used various ML algorithms such as CNN, Naive Bayes, SVM, Random-forest classifier, and Linear Regression. We have analyzed these algorithms by calculating various factors like f1 score, recall, precision, and accuracy. As social media plat-forms continue to evolve, this research field presents exciting opportunities for businesses, researchers, and policymakers to harness the collective voice of the online community and stay informed about the ever-changing public sentiment in the era of digital communication.

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Predicting User Sentiments in Social Media with Machine Learning and Natural Language Processing Techniques

  • Kurmala Lakshmi Sathvika,
  • Devineni Srujitha,
  • Kavuru Tanya,
  • Srilatha Tokala,
  • Murali Krishna Enduri,
  • Satish Anamalamudi

摘要

Social media platforms have become an integral part of modern communication, offering individuals an unprecedented opportunity to express their thoughts, opinions, and emotions publicly. The primary objective of social media trends sentiment analysis is to analyze and interpret the sentiments associated with specific hashtags, topics, or viral content through the application of natural language processing and machine learning algorithms. To analyze the trends, we have used various ML algorithms such as CNN, Naive Bayes, SVM, Random-forest classifier, and Linear Regression. We have analyzed these algorithms by calculating various factors like f1 score, recall, precision, and accuracy. As social media plat-forms continue to evolve, this research field presents exciting opportunities for businesses, researchers, and policymakers to harness the collective voice of the online community and stay informed about the ever-changing public sentiment in the era of digital communication.