• Manjinder Kaur
• Dr. Nirvair Neeru
• Dr. Navjot Kaur

Sentiment Analysis is one of the fastest growing service and application in the mining industry. It is used in integration with machine learning algorithms. More and more industries and organizations have started to use this application in order to achieve better productivity and consumer experience. Different Deep Learning algorithms used for sentiment analysis like Convolutional Neural Network, Recursive Neural Networks and Deep Neural Networks are reviewed for sentiment analysis. There are different levels in sentiment analysis in which it is divided, which can be sentence based, document based or entity based. Sentiment Analysis has different approaches like Lexical Affinity, Statistical Method and Keyword spotting. Sentiment Analysis is divided into different techniques and they may have various sub-techniques. This paper explains the sentiment analysis and various algorithms, approaches related with this rapidly growing application.

Lexicon, Machine Learning, Deep Learning, RNN, CNN, DNN, Semantic Analysis, Keyword Spotting

"SENTIMENT ANALYSIS TECHNIQUES- A REVIEW", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.317-320, June 2019, Available :http://www.jetir.org/papers/JETIR1907348.pdf

"SENTIMENT ANALYSIS TECHNIQUES- A REVIEW", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp317-320, June 2019, Available at : http://www.jetir.org/papers/JETIR1907348.pdf