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@article{151986,
        author = {Kedar Deshpande and Atharv Jakate and Rahul Mahajan and Sanjeev Jadhav},
        title = {Sentiment Analysis Using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {2},
        pages = {115-119},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151986},
        abstract = {Sentiment analysis is one of the most widely known Natural Language Processing (NLP) task which is also known as Opinion mining or emotion AI. It refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Also it is very well known that people have strong feelings may express them in emotionally laden words. Therefore programmers use this information to figure out two parameters: First is Sentiment polarity which shows were exactly the feelings lie on positive side or on the negative one. And secondly Sentiment magnitude which depict how strongly the polarity of that sentiment is. To determine these two main factors the algorithm uses either the Dictionary approach or the Categorization approach. Dictionary approach looks up for polarity and magnitude given the word or phrase and reverse it if there is any negation in it. Whereas Categorization approach learn from examples how to categorize any new piece of text using Machine Learning. Modern day sentiment analysis solutions can provide deeper insight. They can capture what specifically people don’t like about certain things, and afterward can take steps to fix the issue, or improving a process, they can track how that has improved satisfaction percentage rate. They can also differentiate between feedback that is frequent and feedback that influences satisfaction scores. In this paper we will study about different avenues for sentiment analysis mainly Logistic Regression and Naive Bayes using Hotel Reviews Dataset.},
        keywords = {Sentiment, Subjectivity, Polarity, Naive Bayes, Logistic Regression, Classification.},
        month = {},
        }