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All Journal Jurnal EMT KITA Jurnal Penelitian Pendidikan IPA (JPPIPA) IJID (International Journal on Informatics for Development) YUME : Journal of Management Buletin KKN Pendidikan Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) GOVERNANCE: Jurnal Ilmiah Kajian Politik Lokal dan Pembangunan Unram Journal of Community Service (UJCS) Mejuajua International Journal of Management Science and Information Technology (IJMSIT) Journal of Artificial Intelligence and Digital Business Eduvest - Journal of Universal Studies PEDAMAS (Pengabdian Kepada Masyarakat) Journal of Innovative and Creativity Jurnal Manajemen Dayasaing
Muhammad Yusuf Ariyadi
Universitas Muhammadiyah Karanganyar
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Neuroscience Physics Public Health Social Sciences Other

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Journal : IJID (International Journal on Informatics for Development)

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A Hybrid Approach of Pearson Correlation and PCA in Feature Selection for Opinion Mining Tri Romadloni, Nova; Kurniawan, Wakhid; Ariyadi, Muhammad Yusuf; Efendi, Burhan
IJID (International Journal on Informatics for Development) 2025
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study proposes a hybrid feature selection approach that combines Pearson Correlation and Principal Component Analysis (PCA) to improve classification performance in opinion mining tasks. The rapid growth of e-commerce on social media platforms, such as TikTok, has generated a significant volume of user-generated reviews, which are valuable sources of consumer sentiment. However, the high dimensionality of textual data poses challenges in achieving accurate sentiment classification. To address this issue, the proposed method first applies Pearson Correlation to remove irrelevant features with weak correlation to sentiment labels, followed by PCA to reduce dimensionality. The dataset consists of user reviews from the TikTok Seller platform. Experiments using SVM, Naive Bayes, and Random Forest show that the hybrid approach achieves the highest accuracy of 86.2% (SVM and RF), improving over PCA-only by +0.9% and recovering 13.8% accuracy loss for Naive Bayes (from 72.0% to 83.1%). The results demonstrate that integrating correlation- and projection-based methods yields a more compact and effective feature set. This approach is especially suited for opinion mining in noisy, high-dimensional e-commerce data.
A Hybrid Approach of Pearson Correlation and PCA in Feature Selection for Opinion Mining Tri Romadloni, Nova; Kurniawan, Wakhid; Ariyadi, Muhammad Yusuf; Efendi, Burhan
IJID (International Journal on Informatics for Development) Vol. 14 No. 2 (2025): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2025.5195

Abstract

This study proposes a hybrid feature selection approach that combines Pearson Correlation and Principal Component Analysis (PCA) to improve classification performance in opinion mining tasks. The rapid growth of e-commerce on social media platforms, such as TikTok, has generated a significant volume of user-generated reviews, which are valuable sources of consumer sentiment. However, the high dimensionality of textual data poses challenges in achieving accurate sentiment classification. To address this issue, the proposed method first applies Pearson Correlation to remove irrelevant features with weak correlation to sentiment labels, followed by PCA to reduce dimensionality. The dataset consists of user reviews from the TikTok Seller platform. Experiments using SVM, Naive Bayes, and Random Forest show that the hybrid approach achieves the highest accuracy of 86.2% (SVM and RF), improving over PCA-only by +0.9% and recovering 13.8% accuracy loss for Naive Bayes (from 72.0% to 83.1%). The results demonstrate that integrating correlation- and projection-based methods yields a more compact and effective feature set. This approach is especially suited for opinion mining in noisy, high-dimensional e-commerce data.
Co-Authors Ajietama Imam Wiyoga Amirulloh Moh Rumuntoha Anafih, Erwinda Sam Anshori, M Isa Apriliyanto, Erwin Azahro, Kamila Asadiyah Bintang, Rauhulloh Ayatulloh Khomeini Caroline Dimas Fajar Nugroho Efendi, Burhan Fauzan Fadlan, A. Fauziah, Maya Mega Restri Hasni Dyah Kurniawati Amin Wahyudi, Hasni Dyah Kurniawati Herawati, Shabrina Ihsyan kolefiyan Imfrianti Augtiah Indra Pratama Intaniasari, Yossinta Irawati, Diwi Acita Jati, Kurniawan Khoirul Alfian Saleh Kurniawan, Wakhid Lathif, Sefano Dellya Ratu Lutfiah Pratiwi Hasanah Maulana, Mahesa Mohammad Najib Muhammad Amir Anshori Muhammad Ardiansyah Munir, Bagus Misbahul Nandana Pangestu Muwahid Ningrum, Rizky Septiana Nova Tri Romadloni Oroh, Oliviane Puji Astuti Puji Astuti Putra, Romi Iriandi Putri, Dhiajeng Meilind Putri, Dhiajeng Meilinda Rafiqi Bagus Apriyanto,, Maya Mega Restri Fauziah, Ayu Rahmawati Sholihah, Ratnasari Diah Utami Rohana, Shinta Saefudin Saputra, Hendy Jaya Saputri, Ai’syah Larose saraswati25, DyahAyu Sari, Mirna Aulia Sholihah, Ayu Rahmawati Siti Hadiyati Nur Hafida Toni Ahmad Subekti USWATUN HASANAH Wiwit Supriyanti Zaid, Zulfiqar