Twitter Sentiments Analysis Using Machine Learning

Authors

  • Saurabh Singh  Department of Computer Science and Engineering, IMS Engineering College, Ghaziabad ,Uttar Pradesh, India

DOI:

https://doi.org/10.32628/CSEIT206456

Keywords:

Machine learning, Twitter Sentiment Analysis, Natural Language Process, Data Minning, Bag of Words(BoG), Embedded layer, Naïve Bayes Classifier, Keras, Natural language toolkit(nltk).

Abstract

Twitter sentiment analysis is the method of Natural Language Processing (NLP). In this project named Twitter sentiment Analysis we analyze the sentiments behind the twitter’s tweet. We have three type of sentiment: Positive, Neutral and Negative. Analyzing the sentiments behind every tweet is the biggest problem in the early days but now it can be solved with the help of Machine Learning. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters and through the Twitter Sentimental Analysis we can analysis the mood of the person who tweet which can helps in the industries to analyze the market and their product reviews or we can know the sentiments behind the opinion on any topic on which the group of people tweet and through this we can find the final result that the people point on view on the particular topic, product and any other tweets suggestions.

References

  1. Alexander Pak, Patrick Paroubek. Twitter as a Corpus for Sentiment Analysis and Opinion Mining.
  2. Twitter sentiments analyze dataset from Kaggle
  3. Jin Bai, Jian­Yun Nie. Using Language Models for Text Classification.
  4. AnalyticsVidya: For Naïve Bayes, (https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/)
  5. Twitter sentiment analyses report on www.cse.ust.hk
  6. Natural language processing from Wikipedia

Downloads

Published

2020-08-30

Issue

Section

Research Articles

How to Cite

[1]
Saurabh Singh, " Twitter Sentiments Analysis Using Machine Learning" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 4, pp.312-320, July-August-2020. Available at doi : https://doi.org/10.32628/CSEIT206456