Emoji, Text, and Sentiment Polarity Detection Using Natural Language Processing
نویسندگان
چکیده
Virtual users generate a gigantic volume of unbalanced sentiments over various online crowd-sourcing platforms which consist text, emojis, or combination both. Its accurate analysis brings profits to industries and their services. The state-of-art detects sentiment polarity using common sense with text only. research work proposes an emoji-based framework for cognitive–conceptual–affective computing based on the linguistic patterns emojis. proposed emoji text-based parser articulates features along different emojis part speech into n-gram patterns. In this paper, 650 world-famous personages consisting 1,68,548 tweets have been downloaded from across world. results illustrate that natural language processing shows existence in many times seems change overall sentiment. By extension, CLDR name is utilized evaluate patterns, dictionary adopted evaluating text. Eventually, performances three ML classifiers (SVM, DT, Naïve Bayes) are evaluated distinctive features. robust experiments indicate approach outperforms SVM classifier as compared other classifiers. detection generator has achieved exceptional perspective presented sentence by employing flow concept established, features, inversion, coordination, discourse surpassing performance extant state-of-the-art approaches.
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ژورنال
عنوان ژورنال: Information
سال: 2023
ISSN: ['2078-2489']
DOI: https://doi.org/10.3390/info14040222