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All Journal Epsilon: Jurnal Matematika Murni dan Terapan SOCIETAL Majalah Kedokteran Sriwijaya Jurnal Kesehatan Jurnal Hilirisasi IPTEKS (JHI) Menara Ilmu Journal of Industrial Engineering & Management Research (JIEMAR) Jurnal Kesehatan STIKes IMC Bintaro Management and Accounting Expose Jurnal Ilmiah Wahana Pendidikan Welvaart : Jurnal Ilmu Kesejahteraan Sosial Journal of Scientech Research and Development Edu Research : Jurnal Penelitian Pendidikan Menara Pengabdian Pustaka: Jurnal Bahasa dan Pendidikan Jurnal Mahasiswa Manajemen dan Akuntansi KENDALI: Economics and Social Humanities Jurnal Pendidikan Sosial dan Konseling Riset Ilmu Manajemen Bisnis dan Akuntansi Gemeinschaft : Jurnal Masyarakat Pesisir dan Perdesaan Bhinneka: Jurnal Bintang Pendidikan dan Bahasa Jurnal Pengabdian Masyarakat Jurnal Pendidikan, Bahasa dan Budaya Jurnal Penelitian Pendidikan Indonesia (JPPI) Jurnal Kesehatan Jurnal Suara Politik International Journal of Information Engineering and Science KAFEBIS Journal of Global Research in Public Health Welvaart: Jurnal Ilmu Kesejahteraan Sosial As-Sulthan Journal of Education
DEWI ANGGRAINI
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Journal : International Journal of Information Engineering and Science

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Natural Language Processing For Automatic Sentiment Analysis In Social Media Data Siti Rahmawati; Dewi Anggraini; Rizki Kurniawan
International Journal of Information Engineering and Science Vol. 1 No. 1 (2024): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i1.54

Abstract

With the exponential growth of social media platforms, vast amounts of data are generated daily, capturing public opinions, sentiments, and trends in real time. Automatic sentiment analysis using Natural Language Processing (NLP) has emerged as an essential tool to process this data, helping industries, researchers, and policymakers understand social sentiment more effectively. This study explores various NLP techniques for sentiment analysis, including machine learning-based, lexicon-based, and deep learning models. By examining advancements in NLP algorithms and challenges related to language diversity, slang, and context in social media data, this paper highlights the strengths and limitations of current methodologies and discusses potential future directions.
Co-Authors Abdur Razzaq Agung Maulana Irsyad Ahmad Daengs GS Ambar Wati Amin, Suci Amnia Amnia Angga Saputra Ani Siska MY Ani Siska MY Aryuni Salpiana Jabar Asnet Leo Bunga Bahtiar Bahtiar Chris Alberto Amin Dadang Ma'ruf Dedet Deperiky Dewi Marhamah Syadli Dewi Sri Susanti Dhandy Handana Dheo Rimbano Didi Rahmadi Dilgu Meri Elserra Siemin Ciamas Erna Agustina Faisal Faisal Ferry Santoso FLORENCIA Halimatus Sadiah Hanna Marsinta Uli Harsono Teguh Santoso Harto, Toto Hasbullah Ridwan Havidz Aima Ilham Ilham Indra Perdana Islami, Syaifuddin Juhaepa Juhaepa Juliastuti, Dyah Kasrin Kasrin Lena Marianti M. Najib Husain Mey Lys Ceryah Hutasoit Mistiani Puspitasari Muhammad Arsyad Muhammad Yasir Musniwati Harsoyo Nerli Adria Sinabutar Nur Salam Oom Komalasari Pardi Affandi Pariyana Pariyana Paul Diman Putri Aulia Damayanti Raftul Fedri Retno Sugesti Revi Isnabilla Hoirunisa reviisnabilla@gmail.com Riska Susanti Rita Ayu Yolandia Rizki Fatriasi Rizki Kurniawan Robby Mafirli Sekar Wulandari Selvy Fitria Amanda Shindi Sihombing, Putriatdah Sarinah Sirajuddin Siregar, Onan Marakali Siti Rahmawati Sitorus, Arnold Rudolf Siwi Nugraheni Suharty Roslan Susianti Susianti Syaifudin Suhri Kasim Sylvi Tri Andani Tanti Krisnawardhani Tantry Wenny Sitanggang Tri Winahyu Hariyadi Usnia Usnia Vina Kumala Yola Gustifa Yulie Suryani Yuni Yulida