Mining and Analyzing Twitter Trends: Frequency based Ranking of descriptive Tweets
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10.5120/18279-9200 |
Rishabh Jain, Abhishek B.s. and Satvik Jagannath. Article: Mining and Analyzing Twitter Trends: Frequency based Ranking of descriptive Tweets. International Journal of Computer Applications 104(15):24-27, October 2014. Full text available. BibTeX
@article{key:article,
author = {Rishabh Jain and Abhishek B.s. and Satvik Jagannath},
title = {Article: Mining and Analyzing Twitter Trends: Frequency based Ranking of descriptive Tweets},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {104},
number = {15},
pages = {24-27},
month = {October},
note = {Full text available}
}
Abstract
One of the major sources of trending news, events and opinion in the current age is micro blogging. Twitter, being one of them, is extensively used to mine data about public responses and event updates. This paper intends to propose methods to filter tweets to obtain the most accurately descriptive tweets, which communicates the content of the trend. It also potentially ranks the tweets according to relevance. The principle behind the ranking mechanism would be the assumed tendencies in the natural language used by the users. The mapping frequencies of occurrence of words and related hash tags is used to create a weighted score for each tweet in the sample space obtained from twitter on a particular trend.
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