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Authors: Kolambage, Samitha
Tillekeratne, Hasath
Chathuranga, Niroshan
Devendra, Hasanthi
Tissera, Muditha
Keywords: Twitter
Sentimental analysis
machine learning
Graph mining
Data mining
Issue Date: 2017
Publisher: SLIIT
Series/Report no.: ;17-057
Abstract: Hashtag investor is a system that can analyze twitter data to generate useful information including some predictions. Machine learning techniques have been used for this research which falls into data mining to archive sentiment analysis to categorize and identify tweets based on the contents. Twitter has an enormous collection of data. If these data is converted into some useful information, accurate decisions can be made using this data. That is our main objective, which can be very helpful to users, and this system works with respect to four specific objectives. One objective is sentimental analysis of twitter data and finding false tweets. Supervised learning has been used and NLTK and also the naïve Bayes classifier has been used as techniques. The output will be display percentage wise, negative positive and neutral percentages of the given keyword. Twitter data is analyzed according to the given keyword. False tweets identification is done by analyzing user profile. If the user profile criteria does not match with our assumptions this profile is marked as a fake profile. Second objective is comparing two similar products and getting the popularity according to the time. The output is displayed by charts. Similar keywords will be grouped. Clustering algorithms has been used for grouping. Our forth objective is finding some latest ongoing events and the number of users who were active at certain time periods, ARIMA model has been used as the technique. Our final objective is to analyze retweets comments and tweets on particular two products. Output is displayed as a graph. Propagation topology is used as the technique for retweet analysis and exponential regression function is used for popularity prediction
Appears in Collections:SLIIT Student Research -2017

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