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Sentiment Predictions Using Deep Belief Networks Model for Odd-Even Policy in Delhi

Sentiment Predictions Using Deep Belief Networks Model for Odd-Even Policy in Delhi

Sudhir Kumar Sharma, Ximi Hoque, Pravin Chandra
Copyright: © 2016 |Volume: 7 |Issue: 2 |Pages: 22
ISSN: 1947-9093|EISSN: 1947-9107|EISBN13: 9781466691414|DOI: 10.4018/IJSE.2016070101
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MLA

Sharma, Sudhir Kumar, et al. "Sentiment Predictions Using Deep Belief Networks Model for Odd-Even Policy in Delhi." IJSE vol.7, no.2 2016: pp.1-22. http://doi.org/10.4018/IJSE.2016070101

APA

Sharma, S. K., Hoque, X., & Chandra, P. (2016). Sentiment Predictions Using Deep Belief Networks Model for Odd-Even Policy in Delhi. International Journal of Synthetic Emotions (IJSE), 7(2), 1-22. http://doi.org/10.4018/IJSE.2016070101

Chicago

Sharma, Sudhir Kumar, Ximi Hoque, and Pravin Chandra. "Sentiment Predictions Using Deep Belief Networks Model for Odd-Even Policy in Delhi," International Journal of Synthetic Emotions (IJSE) 7, no.2: 1-22. http://doi.org/10.4018/IJSE.2016070101

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Abstract

This paper analyzes the odd-even policy in Delhi using tweets posted on Twitter from December 2015 to August 2016. Twitter is a social network where users post their feelings, opinions and sentiments for any event. This paper transforms the unstructured tweets into structured information using open source libraries. Further objective is to build a model using Deep Belief Networks classification (DBN) to classify unseen tweets on the same context. This paper collects tweets on this event under six hashtags. This study explores three freely available resources / Application Programming Interfaces (APIs) for labeling of tweets for academic research. This paper proposes three sentiment prediction models using the sentiment predictions provided by three APIs. DBN classifier is used to build six models. The performances of these six models are evaluated through standard evaluation metrics. The experimental results reveal that the TextBlob API and proposed Preference Model outperformed than the other four sentiment prediction models.

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