Real Time Opinion Polarity Detection in Blogs by Weighted Ranking TF-IDF Algorithm
Abhishek Tiwari, Upasna Tiwari and Narendra S Chaudhari. Article: Real Time Opinion Polarity Detection in Blogs by Weighted Ranking TF-IDF Algorithm. IJCA Special Issue on Optimization and On-chip Communication ooc(1):40-44, February 2012. Full text available. BibTeX
@article{key:article,
author = {Abhishek Tiwari and Upasna Tiwari and Narendra S Chaudhari},
title = {Article: Real Time Opinion Polarity Detection in Blogs by Weighted Ranking TF-IDF Algorithm},
journal = {IJCA Special Issue on Optimization and On-chip Communication},
year = {2012},
volume = {ooc},
number = {1},
pages = {40-44},
month = {February},
note = {Full text available}
}
Abstract
Blogs are mainly posted in languages where users may not always use accurate and exact grammatically correct language and sometimes short form of the words and sentences are used. this work proposes a unique technique of opinion polarity mining from both RSS feed and stored blog posts without using machine learning and with the help of forward scanning algorithm i.e. TF-IDF[15]. The method first finds the similarity of certain blogs with a particular topic. If the blogs are closely related with a topic, the presence of opinion words and sentences are detected in the blogs. If such sentences are found, their appearance specific meaning is extracted. A scoring technique is proposed which finally extracts the polarity of the opinionistic blog. The algorithm is tested with yahoo posts and the results shows an overall accuracy of about 79% in classifying the opinion
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