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Sentimental Analysis with Emotion Detection using Prediction Algorithm


Alsaba Naaz , ACET; Jayta Kelzare, ACET; Neha Dongre, ACET; Khushboo Ansari, ACET; Ritesh Shrivastav, ACET


Microblogging, Accessibility, Clustering, Labeling


The volume of microblogging messages is increasing exponentially with the popularity of microblogging services. With a large number of messages appearing in user interfaces, it hinders user accessibility to useful information buried in disorganized, incomplete, and unstructured text messages. In order to enhance user accessibility, we propose to aggregate related microblogging messages into clusters and automatically assign them semantically meaningful labels. However, a distinctive feature of microblogging messages is that they are much shorter than conventional text documents. These messages provide inadequate term co-occurrence information for capturing semantic associations. To address this problem, we propose a novel framework for organizing un structured microblogging messages by transforming them to a semantically structured representation. The proposed first captures informative tree fragments by analyzing a parse tree of the message, and then exploits external knowledge bases to enhance their semantic information. Twitter dataset shows that our framework significantly outperforms existing state-of-the-art methods.

Other Details

Paper ID: IJSRDV6I21123
Published in: Volume : 6, Issue : 2
Publication Date: 01/05/2018
Page(s): 1605-1607

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