Journal article Open Access

Intelligent Estimation of Social Media Sentimental Features using Deep Learning with Natural Language Processing Strategies

Sarojini Yarramsetti; Anvar Shathik J; Renisha P. S

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:contributor>Blue Eyes Intelligence Engineering  and Sciences Publication(BEIESP)</dc:contributor>
  <dc:creator>Sarojini Yarramsetti</dc:creator>
  <dc:creator>Anvar Shathik J</dc:creator>
  <dc:creator>Renisha P. S</dc:creator>
  <dc:description>In this digital world, experience sharing, knowledge exploration, taught posting and other related social exploitations are common to every individual as well as social media/network such as FaceBook, Twitter, etc plays a vital role in such kinds of activities. In general, many social network based sentimental feature extraction details and logics are available as well as many researchers work on that domain for last few years. But all those research specification are narrowed in the sense of building a way for estimating the opinions and sentiments with respect to the tweets and posts the user raised on the social network or any other related web interfacing medium. Many social network schemes provides an ability to the users to push the voice tweets and voice messages, so that the voice messages may contain some harmful as well as normal and important contents. In this paper, a new methodology is designed called Intensive Deep Learning based Voice Estimation Principle (IDLVEP), in which it is used to identify the voice message content and extract the features based on the Natural Language Processing (NLP) logic. The association of such Deep Learning and Natural Language Processing provides an efficient approach to build the powerful data processing model to identify the sentimental features from the social networking medium. This hybrid logic provides support for both text based and voice based tweet sentimental feature estimations. The Natural Language Processing principles assists the proposed approach of IDLVEP to extracts the voice content from the input message and provides a raw text content, based on that the deep learning principles classify the messages with respect to the estimation of harmful or normal tweets. The tweets raised by the user are initially sub-divided into two categories such as voice tweets and text tweets. The voice tweets will be taken care by the NLP principles and the text enabled tweets will be handled by means of deep learning principles, in which the voice tweets are also extracted and taken care by the deep learning principle only. The social network has two different faces such as provides support to developments as well as the same it provides a way to access that for harmful things. So, that this approach of IDLVEP identifies the harmful contents from the user tweets and remove that in an intelligent manner by using the proposed approach classification strategies. This paper concentrates on identifying the sentimental features from the user tweets and provides the harm free social network environment to the society.</dc:description>
  <dc:source>International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10(6) 74-79</dc:source>
  <dc:subject>Deep Learning, Natural Language Processing, Sentimental Features, Opinion Mining.</dc:subject>
  <dc:subject>Retrieval Number</dc:subject>
  <dc:title>Intelligent Estimation of Social Media  Sentimental Features using Deep Learning with  Natural Language Processing Strategies</dc:title>
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