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Sentiment Analysis And Pictorial Depiction Of Tweets Using MLTA

  • Unique Paper ID: 170707
  • Volume: 11
  • Issue: 7
  • PageNo: 283-286
  • Abstract:
  • In politics, social lores or in marketable conditioning same rules apply, the only and the most important questions – ‘ how the target population feels or is going to feel towards some textbook.’ A fashion known as sentiment analysis( SA) makes it possible to dissect different sized pieces of textbook in a natural language and unequivocally classify them into positive, negative and neutral with the help of both statistical/ verbal as well as deep literacy ways. nonetheless, there's an absence of similar tools which allow for analysis of a set of disconnected textbooks and concentrate on the overall emotion of the set. This paper, in this environment, innovates and proposes an algorithm, called Multilayered Tweet Analyzer( MLTA) that utilizes amulti-layered network- grounded graph model of social media textbooks to further strengthen the encoding of connections connecting different embeds of tweets. Other models of representation fail to repel comparison with graph structures. State of the art Graph Neural Networks( GNNs) are also employed to different important information from the TweetMLN and use that for vaticination of the uprooted graph. The MLTA model is salutary comparatively as it allows the druggies to elect from colorful possible negative and positive feelings. also, the model can give vaticination on Twitter data at group position when there's an allowance for accurate depression
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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 7
  • PageNo: 283-286

Sentiment Analysis And Pictorial Depiction Of Tweets Using MLTA

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