• Kumud shende
• Kalyani Waghmare

This survey paper review based on the Lifelong learning sentiment analysis for social media texts. Lifelong learning means the people continuous learning process or lifelong learning paper aims to learn as peoples do: retain the learned knowledge from the previous task and use it to help in the future learning task. In the social media that contain a large range amount of text and a large range of topics, so it would be very difficult to manually collect enough labeled data to train a different sentiment classifier for different domains. In social media, the text is continuously increasing and constantly changing the topics. So we only focused on large scale data-sets and sentiment analysis different technique. Now a day users are relying on social media, so the importance of a review is going higher. Sentiment analytic thinking plays a classifying increasingly more important role in the user’s opinion, attitude, and expressed their feeling in a text, so we focused sentiment analysis. But in machine learning, going through one thousand text reviews would be much easier, if any model is used to polarize those reviews and learn from it. We used Various algorithms in the literature survey like Naive Bayes’s, support vector machine and Maximum Entropy for the lifelong long learning sentiment analysis. In this paper, a brief survey is carried out on various data mining and machine learning algorithms for Lifelong learning for Large-Scale Social Media Sentiment Analysis. A method on a large scale data-sets to polarize different classification like positive or negative and gives better accuracy.

Sentiment analysis, lifelong learning, social media analysis, NLP

"Survey on Lifelong Learning for Large-Scale Social Media Sentiment Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.232-240, May-2019, Available :http://www.jetir.org/papers/JETIR1905C35.pdf

"Survey on Lifelong Learning for Large-Scale Social Media Sentiment Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp232-240, May-2019, Available at : http://www.jetir.org/papers/JETIR1905C35.pdf