DocumentCode :
702736
Title :
Social media online opinion summarization using ensemble technique
Author :
More, Mugdha ; Tidke, Bharat
Author_Institution :
Dept. of Comput. Eng., Flora Inst. of Technol., Pune, India
fYear :
2015
fDate :
8-10 Jan. 2015
Firstpage :
1
Lastpage :
6
Abstract :
Today, the world is going towards web due to tremendous growth of Internet. People are taking a help of opinions from web for getting ideas to start a new business, improving its current business or taking knowledge about particular thing from different point of views. Opinions are not always true or beneficial since people write opinion based on its own behavior, emotions, experience which makes people confused by seeing various types of opinions and get failed to take right decision. Therefore, there are needs for summarization of such fraudulent opinions and has become great challenge in today´s e-world. This paper proposed robust feature-based opinion summarization system based on weighting scheme and association rule after preprocessing techniques, also ensemble technique used for feature extraction and finally finding out the orientation of extracted features and then display the summary of reviews.
Keywords :
Internet; data mining; social networking (online); text analysis; Internet; association rule; e-world; ensemble technique; feature extraction; fraudulent opinions summarization; preprocessing techniques; robust feature-based opinion summarization system; social media online opinion summarization; weighting scheme; Association rules; Feature extraction; Semantics; Sentiment analysis; Unsolicited electronic mail; Association Rule; Ensemble; Feature Extraction; Opinion; Reviews; Weighting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pervasive Computing (ICPC), 2015 International Conference on
Conference_Location :
Pune
Type :
conf
DOI :
10.1109/PERVASIVE.2015.7087112
Filename :
7087112
Link To Document :
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