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@article{170707,
        author = {Jala Kavya and Halder Kishore and Gerupati  Jithander and K.Akshitha},
        title = {Sentiment Analysis And Pictorial Depiction Of Tweets Using MLTA},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {283-286},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170707},
        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},
        keywords = {},
        month = {December},
        }