• Akshara Saji
• Abila Saravanabhavan
• Balakirthika Esaikkiappan
• Anushka Agashe

Machine learning has certainly changed how data is perceived and utilized. With this progress the fine dining experience has lept into a new phase. With social media, people have started being more vocal about their experiences about dining at restaurants by writing reviews. Sentiment analysis of the reviews determines the polarity of the population towards various restaurants. Along with it, aspect extraction deduce the relevant features about the restaurants as stated in the reviews. In this project we exploit deep learning algorithms to extract the general sentiment and classification of all specific features of restaurants in the reviews. We propose a Recurrent neural network (RNN) - Bidirectional LSTM model to do classification and aspect extraction of reviews by Convolutional neural network (CNN) algorithm. Also we have conducted a comparative analysis of RNN and CNN algorithms for sentiment analysis.

Aspect based sentiment analysis (ABSA), Bidirectional Long Short Term Memory (Bi - LSTM),Convolutional neural network (CNN),Deep learning(DL),Natural Language Processing (NLP), Recurrent neural network (RNN), Sentiment analysis (SA)

"STUDY OF DEEP LEARNING ALGORITHMS FOR SENTIMENT ANALYSIS AND ASPECT EXTRACTION OF RESTAURANT REVIEWS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.405-409, April-2019, Available :http://www.jetir.org/papers/JETIR1904L60.pdf

"STUDY OF DEEP LEARNING ALGORITHMS FOR SENTIMENT ANALYSIS AND ASPECT EXTRACTION OF RESTAURANT REVIEWS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp405-409, April-2019, Available at : http://www.jetir.org/papers/JETIR1904L60.pdf