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All Journal Bulletin of Electrical Engineering and Informatics International Journal of Multidisciplinary: Applied Business and Education Research
Arcinas, Myla M.
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Medicine & Pharmacology

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Journal : Bulletin of Electrical Engineering and Informatics

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Optimized convolutional neural network enabled technique for sentiment analysis from social media data Veena, Chinta; Sultanpure, Kavita A.; Meenakshi, Meenakshi; Bangare, Sunil L.; Raskar, Punam Sunil; Sadashiv Kulkarni, Shriram; Arcinas, Myla M.; Rane, Kantilal Pitambar
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.7712

Abstract

Sentiment analysis is an area of computational linguistics that studies natural language processing. The most significant subtasks are gathering people's thoughts and organizing them into groups to determine how they feel. The primary purpose of sentiment analysis is to determine whether the individual who created a piece of material has a positive or negative opinion about a subject. It has been claimed that sentiment analysis and social media mining have contributed to the recent success of both private sector and the government. Emotional analysis has applications in practically every aspect of modern life, from individuals to corporations, telecommunications to medical, and economics to politics. This article describes an improved sentiment analysis model based on gray level co-occurrence matrix (GLCM) texture feature extraction and a convolutional neural network (CNN). This model was created using tweets. First, texture characteristics are extracted from the input data set using the GLCM technique. This feature extraction improves categorization accuracy. CNNs are used to classify objects. It outperforms both the support vector machine and the AdaBoost algorithms in terms of accuracy. CNN has achieved an accuracy of 98.5% for sentiment analysis task.
Co-Authors Abrenica , Keona Faye C. Acosta, Arabella R. Afable, Trisha Mae M. Aguilar, Chelsea Dominique C. Ahorro , Adrian Miguelle T. Alberto, Raziel Alodia L. Amparo, Alexa Louise S. Angeles, Audrey Carmela C. Arenas, Samantha Ashley C. Bangare, Sunil L. Basto, Orange Q. Berana, Xzareena Christianne L. Bermal, Marian Lucille N. Bracamonte, Samantha Nicole R. Cabotaje, Angela Nicole M. Cai, Kimberly B. Carlos, Meya Pauline A. Chan, Jaeyanne A. Chavez, Katrina Althea P. Chua, Ryanne Pauline S. Co, Maegan Helaena G. Correa, Olivia Maria D. Cruz, Mary Julianne T. Dela David , Ma. Rafaella T. Declaro, Maria Kaeysha I. Del Rosario, Aliyah Monina J. Dela Cruz, Rafya Jose P. Delfin , Riane Jesserine M. Deomampo, Sophia Isabelle D. Espina, Rochelle Margaux I. Gaerlan, Paolo Miguel S. Gaticales, Natalia P. Go, Chloe Nadine D. Golosino, Benecia Loyd V. Gomez, Thrissa Marie Guevara, Sofia Kristen D. Inocando, Trina Marie C. Jose, Johanna Louise C. Ke, Sam Wei Quan P. Lamberto, Jilian Casandra D. Lim, Sophia Gabrielle S. Lopez, Kristoffer Romulo B. Maceda, Danela Kayla T. Malgapu, Mia Gayle S. Manaois, Alexa D. Marco, Daiseree A. Meenakshi, Meenakshi Molina, Joaquin Maria V. Neves, Aerin Paulina B. Ng, Trixia Anne Nicole P. Padama, Angela Marie D. Pangilinan, Aimee Breanna Y. Paraon, Raichel Joy R. Provido, Alliyah Vanessa C. Rane, Kantilal Pitambar Raskar, Punam Sunil Sadashiv Kulkarni, Shriram Salazar, Jeremy Kirsten R. Salazar, Rianna Marie C. Samson, Julia Carelle M. Santelices, Samantha Mae B. Seno, Marianne Rose T. Suarez, Jesley Eryne E. Sultanpure, Kavita A. Tan, Hanna Lynn D. Tugade, Guinevere Yvonne G. Umandap, Ashley Nicole S. Veena, Chinta Villamonte, Gabriella M. Y. Go, Jenina Paula