• KONDAVEETI SRI VENKATESH
• SREEJA SRIRAM

This is a Project based on Text Mining of Sentiment Analysis which provides a novel approach to identify and classify opinions called sentiments from source text. It is performed by analysing tweets from Twitter and information from Wikipedia. The User Interface allows users to enter a name of any famous Personality with a valid twitter account or a page in Wikipedia .The input for twitter textbox is used to retrieve the recent hundred tweets, which can be up to a maximum of 280 characters in length from twitter using Authentication Keys. The input for wikipedia textbox fetches data from Wikipedia and conducts pre-processing. Pre-processing is executed on the raw data through Regular Expressions to remove noisy data like URL’s, special characters etc., to make them clean and well organised. The challenge is to gather all such relevant data, perform predictions and calculate the polarity which is critical for decision making. Polarity refers to be the emotion expressed in a sentence, which can be categorised as positive, negative or neutral ranging from -1 to +1. Finally, the output of polarity is exhibited in the form of a pie chart and graph. This sort of analysis can mainly be advantageous for organisations, where status of the organisation can be known by simply having a look at the graph/pie chart saving a lot of valuable time rather than going through all the reviews in the form of text.

Tweet, Sentiment Classification, Wikipedia, Common Gateway Interface, Sentiment Analysis, Text Mining

"Opinion Sentiment Analysis on Twitter and Wikipedia", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.484-491, November-2018, Available :http://www.jetir.org/papers/JETIR1811671.pdf

"Opinion Sentiment Analysis on Twitter and Wikipedia", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp484-491, November-2018, Available at : http://www.jetir.org/papers/JETIR1811671.pdf