Main Article Content
As the increase of social networking, people started to share information through different kinds of social media. Sentiment Analysis, a Natural Language processing helps in finding the sentiment or opinion hidden within a text. Sentiment analysis is an approach to analyze data and retrieve sentiment that it embodies. Twitter sentiment analysis is an application of sentiment analysis on data from Twitter (tweets), in order to extract sentiments conveyed by the user. It is an important source of decision making and can be extracted, identified, evaluated from the online sentiments reviews. The main goal of the proposed framework is to connect on Twitter and search for the tweets that contain a particular keyword and then evaluate the polarity of the tweets as positive and negative. In order to select the best features Chi Square test is used and Na�ve Bayes classifier is used for training and testing the features and also evaluating the sentimental polarity. The proposed system is implemented using Python.
How to Cite
, K. K. K. K. S. D. M. S. (2018). Micro-blogging Sentimental Analysis on Twitter Data Using Na�ve Bayes Machine Learning Algorithm in Python. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 4(4), 46–51. Retrieved from https://ijfrcsce.org/index.php/ijfrcsce/article/view/1465