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Additional resources for Big Data Analytics: Third International Conference, BDA 2014, New Delhi, India, December 20-23, 2014. Proceedings

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Precision versus Recall map is also shown for maximum entropy classifier in Fig. 12. Here we see that by using 2-Step classifier, the Recall of “Negative” and 50 Y. Garg and N. Chatterjee “Positive” classes has increased, at the cost of recall of “Neutral” class. We also notice that the difference in Precision of various classes has increased. Fig. 12. Precision vs. Recall for Maximum Entropy Classifier 5 Conclusion In this paper, we created a sentiment classifier for twitter using labelled data sets.

The regular expression used to find out handles is @(\w+) and the replaced expression is HNDL_\1 URLs. Users often share hyperlinks in their tweets. co/FCWXoUd8 – such links also enables Twitter to alert users if the link leads out of its domain. From the point of view of text classification, a particular URL is not important. However, presence of a URL can be an important feature. /]+ which we replace by a simple word, URL. Emoticons. Use of emoticons is very prevalent throughout the web, more so on micro-blogging sites.

Authors proposed to use a combination of relational (for mandatory attributes) and other models (EAV/ dynamic tables/ OEAV/ OCOM for optional attributes) for storing standardized EHRs. The current research has experimentally shown the memory consumed and time taken to execute the basic queries by the various models. Experiment in this research work was conducted on a non-standardized EHRs database. In future authors aim to simulate the same experiment on standardized EHRs database. Applicability of results retrieved from the current research are not limited to EHRs only and can be used for deciding the model that should be opted for any type of BIG DATA such as finance, business informatics, streaming data, social media content, astronomy surveys, genomic and proteomic studies.

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