Textual Dissection of Live Twitter Reviews using Naive Bayes

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Abstract

Textual dissection can be a very useful aspect for the extraction of useful information from text documents. The ideology of textual dissection is the way people think about a particular text. It is the process where given reviews are classified as positive or negative. A huge amount of data (reviews) is present on the web which can be analyzed to make it useful. It can prove to be useful specifically for marketing, business, polity as it allow us to do easy analysis of the subject under consideration. In today's era of internet, lots and lots of people can connect with each other. Internet has made it possible for us to connect and find out the opinions dissection. Internet has provided a lot of platform through which opinions from different people can be taken through Forums, Blogs, and Social networking sites. This paper proposes the use of Tweepy and TextBlob as a python library to access and classify Tweets using Naïve Bayes, a Machine Learning technique. Our Technique is meant to ease out the process of analysis, summarization and classification.

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APA

Kunal, S., Saha, A., Varma, A., & Tiwari, V. (2018). Textual Dissection of Live Twitter Reviews using Naive Bayes. In Procedia Computer Science (Vol. 132, pp. 307–313). Elsevier B.V. https://doi.org/10.1016/j.procs.2018.05.182

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