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    "title": "SARCAMNET: EXTENSION OF LEXICON ALGORITHM FOR EMOJI-BASED SARCASM DETECTION FROM TWITTER DATA",
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            "published": "2024-01-25",
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            "value": "Lexicon algorithm is used to determine the sentiment expressed by a textual content. This sentiment might be negative, neutral, or positive. It is possible to be sarcastic using only positive or neutral sentiment textual contents. Hence, lexicon algorithm can be useful but insufficient for sarcasm detection. It is necessary to extend the lexicon algorithm to come up with systems that would be proven efficient for sarcasm detection on neutral and positive sentiment textual contents. In this paper, two sarcasm analysis systems both obtained from the extension of the lexicon algorithm have been proposed for that sake. The first system consists of the combination of a lexicon algorithm and a pure sarcasm analysis algorithm. The second system consists of the combination of a lexicon algorithm and a sentiment prediction algorithm. Finally, naive bayes are used to predict sarcasm detection using pretrained features.",
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                "unstructured": "Cambridge University Press, 2023. Sarcasm. [Online] Available At: Https://Dictionary. Cambridge.Org/Dictionary/English/Sarc Asm [Accessed 20 January 2018]"
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                "key": "ref2",
                "unstructured": "Palanisamy, P., Yadav, V., & Elchuri, H. (2023). Serendio: Simple And Practical Lexicon-Based Approach To Sentiment. 543-548"
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                "key": "ref3",
                "doi": "10.1186/s13388-015-0024-x",
                "unstructured": "Jurek, A., Mulvenna, M. D., & Bi, Y. (2021). Improved Lexicon-Based Sentiment Analysis For Social Media Analytics. Springeropen, 4-9"
            },
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                "key": "ref4",
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            },
            {
                "key": "ref5",
                "unstructured": "Rathan, K., & Suchithra, R. (2022). Sarcasm Detection Using Combinational. Imperial Journal Of Interdisciplinary Research, 546-551"
            },
            {
                "key": "ref6",
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            },
            {
                "key": "ref7",
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            },
            {
                "key": "ref8",
                "unstructured": "Haripriya, V., & Patil, D. P. (2022). A Survey Of Sarcasm Detection In Social Media. International Journal For Research In Applied Science & Engineering Technology, 1748-1753"
            },
            {
                "key": "ref9",
                "unstructured": "Musto, C., Semeraro, G., & Polignano, M. (n.d.). A Comparison Of Lexicon-Based Approaches For Sentiment Analysis Of Microblog Posts. Figure 1. System Architecture RESULT Above screen descibes sarcasm result based on input dataset. Above screen discribed sarcasm content like positive and nagetive. CONCLUSION The aim of this study was to propose ways to extend the lexicon algorithm to build systems that would be Dr. SUBBA REDDY BORRA, T. KRUTHIKA, T.SAI POOJITHA, P. DEEPIKA: SARCAMNET: EXTENSION OF LEXICON ALGORITHM FOR EMOJI-BASED SARCASM DETECTION FROM TWITTER DATA"
            },
            {
                "key": "ref10",
                "unstructured": "Saxena, R., 2023. How The Naive Bayes Classifier Works In Machine Learning. [Online] Available At: Http://Dataaspirant.Com/2023/02/06/ Naivebayes-Classifier-Machine-Learning/ [Accessed 10 February 2023]"
            },
            {
                "key": "ref11",
                "unstructured": "Gandhi, R., 2022. Naive Bayes Classifier. [Online] Available At: Https://Towardsdatascience.Com/ Naivebayes-Classifier-81d512f50a7c [Accessed 10 February 2023]"
            },
            {
                "key": "ref12",
                "unstructured": "Ray, S., 2023. 6 Easy Steps To Learn Naive Bayes Algorithm (With Codes In Python And R). [Online] Available At: Https://Www.Analyticsvidhya. Com/Blog/2023/09/Naivebayesexplained/ [Accessed 10 February 2023]"
            },
            {
                "key": "ref13",
                "unstructured": "Aggarwal, S., & Kaur, D. (2023). Naïve Bayes Classifier With Various Smoothing. International Journal Of Computer Trends And Technology, 873-876"
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                "key": "ref14",
                "unstructured": "Subba Reddy Borra, G. Jagadeeswar Reddy And E. Sreenivasa Reddy, “Fingerprint Image Compression Using Wave Atom Transform”, International Journal Of Advanced Computing, Vol. 48, No. 1, 2015"
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                "doi": "10.1016/j.aci.2017.07.001",
                "unstructured": "Subba Reddyborra, G.Jagadeeswar Reddy, E.Sreenivasa Reddy, “Classification Of Fingerprint Images With The Aid Of Morphological Operation And Agnn Classifier”, Applied Computing And Informatics, 2017"
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                "unstructured": "Subba Reddy Borra, Akshaya, B. Swathi, B. Sraveena, B. Satya Sahithi. (2023). Machine Learning Algorithms-Based Prediction Of Botnet Attack For Iot Devices. Turkish Journal Of Computer And Mathematics Education (Turcomat), 14(03), 65–78. Https://Doi. Org/10.17762/Turcomat.v14i03.13938"
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                "unstructured": "Subba Reddy Borra, B Gayathri, B Rekha, B Akshitha, B. Hafeeza. (2023). K-Nearest Neighbour Classifier For Url-Based Phishing Detection Mechanism. Turkish Journal Of Computer And Mathematics Education (Turcomat), 14(03), 34–40. Https://Doi. Org/10.17762/Turcomat.v14i03.13935"
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                "unstructured": "Dr. Subba Reddy Borra, K.Harshitha, Kudithi Neha, K. Sindhusha, K. Akshaya. (2023). Block Chain Based Agriculture Crop Delivery Platform For Enforcing Transparency. Turkish Journal Of Computer And Mathematics Education (Turcomat), 14(2), 987–997. Https://Doi. Org/10.17762/Turcomat.v14i2.13925"
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                "doi": "10.61841/turcomat.v14i03.14521",
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                "unstructured": "Dr. B. Subba Reddy, S. Shresta, S. Sathhvika, P. Lakshmi Manasa Shreya. (2022). Role Of Machine Learning In Education: Performance Tracking And Prediction Of Students. Turkish Journal Of Computer And Mathematics Education (Turcomat), 13(03), 854–862. Https://Doi. Org/10.17762/Turcomat.v13i03.13175"
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