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Human Action Recognition Using Trajectory-Based Spatiotemporal Descriptors Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Efficient and Parallel Framework for Analyzing the Sentiment

Authors : Ankur Sharma, Gopal Krishna Nayak

Published in: Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications

Publisher: Springer Singapore

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Abstract

With the advent of Web 2.0, user-generated content is led to an explosion of data on the Internet. Several platforms such as social networking, microblogging, and picture sharing exist that allow users to express their views on almost any topic. The user views express their emotions and sentiments on products, services, any action by governments, etc. Sentiment analysis allows quantifying popular mood on any product, service or an idea. Twitter is popular microblogging platform, which permits users to express their views in a very concise manner. In this paper, a new framework is crafted which carried out the entire chain of tasks starting with extraction of tweets to presenting the results in multiple formats using an ETL (Extract, Transform, and Load) big data tool called Talend. The framework includes a technique to quantify sentiment in a Twitter stream by normalizing the text and judge the polarity of textual data as positive, negative, or neutral. The technique addresses peculiarities of Twitter communication to enhance accuracy. The technique gives an accuracy of above 84% on standard datasets.

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Metadata
Title
Efficient and Parallel Framework for Analyzing the Sentiment
Authors
Ankur Sharma
Gopal Krishna Nayak
Copyright Year
2017
Publisher
Springer Singapore
Book
Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications

Print ISBN: 978-981-10-3152-6

Electronic ISBN: 978-981-10-3153-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-3153-3_14

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