Efficient Deep Learning Approach for Dimensionality Reduction using Micro blogs from Big data
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 3
Abstract
Nowadays Information Technology plays a vital role in every aspects of the human life. Now a world, the huge amount of stored information has been enormously increasing day by day which is generally in the unstructured form and cannot be used for any processing to extract useful information. Exploring potentially useful information from huge amount of textual data produced by micro blogging services has attracted much attention in recent years. An important preprocessing step of micro blog text mining is to convert natural language texts into proper numerical representations. Due to the short-length characteristics of micro blog texts, using term frequency vectors to represent micro blog texts will cause “sparse data” problem. Finding proper representations of micro blog texts is a challenging issue. In this project, we apply deep learning networks to map the high-dimensional representations of micro blog texts to low-dimensional representations. To improve the result of dimensionality reduction, we take advantage of the semantic similarity derived from two types of micro blog specific information, namely the retweet relationship and hash tags. Two types of approaches, including modifying training data and modifying the training objective of deep networks, are proposed to make use of micro blog-specific information. To improve the efficiency we implement the system in Hadoop. In addition to that to make services effective. To achieve the scalability and efficiency with help of map reduce framework in a big data environment.
Authors and Affiliations
Mr. M. Vengateshwaran, Mrs. C. Ramyapriyadarsini, Ms. N. Valarmathi
Keywords
Related Articles
- EP ID EP23199
- DOI http://doi.org/10.22214/ijraset.2017.3002
- Views 241
- Downloads 6
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