• Omkar Patil Karade
• Siddhant Gangakhedkar
• Shubham Darade
• Sarang Chandak

The popularity of social sites like Twitter, Facebook, and Instagram etc. is increasing rapidly. Huge number of tweets and short messages are being posted every single minute by the users from throughout the world, hence size of the data is very huge. As the data is incoming simultaneously from various sources, it is very critical and time taking to analyze and understand the meaning of data. Redundancy and noise is present in this data at a large extent. This must be removed by using Sentiment Analysis and Summarization of tweets to get the meaningful information. This will help users to get necessary information in short time. Sentiment analysis is contextual mining of text which identifies and extracts subjective information in source material. Initially through HTTP request-response, tweets get extracted from websites by searching keyword called Web Scraping. Preprocessing is done on extracted data in order to remove unwanted data such as stop words, suffixes, punctuation and conjunctions. Term Frequency (TF) and Inverse Document Frequency (IDF) is calculated for each word to know the frequency and importance of word in the document. K-Means clustering is performed on text data to get Tweet Cluster Vectors. The regular approaches of the summarization depends on the static or offline data. We removed this complexity of data and generated simple raw summarized text by using clustered data. Finally the accuracy is calculated based on tweets and type of the result is displayed.

Sentiment Analysis, Web Scraping, Term Frequency, Inverse Document Frequency. K-Means clustering.

"Sentiment Analysis and Summarization of Tweets with timeline generation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.352-357, May-2019, Available :http://www.jetir.org/papers/JETIR1905N53.pdf

"Sentiment Analysis and Summarization of Tweets with timeline generation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp352-357, May-2019, Available at : http://www.jetir.org/papers/JETIR1905N53.pdf