• Apurva Dadhwal
• K.L Bansal

The available data on social media has contributed to vast research using sentiment analysis. The twitter-based social media represents a gold-mine approach for analyzing the performance of the brand. Large opinions of the people are found over Twitter that are honest, informative, and casual as compared to the formal type of data-survey analysis using magazines or reports. Millions of people share and express their sentiments over the media discussing about the brands whom they interact with. When such type of sentiments are identified over the media, then the information gained from such sentiments represents fruitful results benefiting large companies or organizations Sentiment analysis has turned out one of the most significant tools in natural language processing because it opens up numerous possibilities to understand people’s sentiments on different topics. The purpose of an aspect-based sentiment analysis is to understand this further and find out what someone is talking about, and whether he likes it or does not like it. There have been various ways to deal with handle this issue, utilizing machine learning. In this thesis, labeled data is used on the basis of polarity and Tweets preprocess and extract unigram features after preprocessing of the tweets.

SA, TSA, POS, SVM, KNN, NLP, LSTM

"REVIEW ON TWEETS SENTIMENT ANALYSIS BY MACHINE LEARNING APPROACHES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.323-335, December-2018, Available :http://www.jetir.org/papers/JETIR1812143.pdf

"REVIEW ON TWEETS SENTIMENT ANALYSIS BY MACHINE LEARNING APPROACHES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp323-335, December-2018, Available at : http://www.jetir.org/papers/JETIR1812143.pdf