An Ensemble-Classifier Based Approach for Multiclass Emotion Classification of Short Text

Chawla, Shivangi; Mehrotra, Monica

doi:10.1109/icrito.2018.8748757

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Data tersebut merupakan data yang diambil dari 8.567 data komentar mahasiswa yang telah ditentukan muatan emosinya. Muatan emosi yang digunakan sebagai label adalah 8 jenis emosi Plutchik [23], yaitu marah (anger), antisipasi (anticipation), muak (disgust), takut (fear), gembira (joy), sedih (sadness), terkejut (surprise) dan percaya/penerimaan (trust/acceptance). Cara melabeli dilakukan secara manual oleh oleh 2 orang ahli bahasa.…”

Section: Pengumpulan Dataunclassified

Klasifikasi Emosi Berbasis Emolex dari Komentar Evaluasi Akademik Mahasiswa

Hamzah,

Ariyana

2024

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Upaya melakukan analisis emosi pada teks komentar mahasiswa dalam evaluasi pembelajaran sangat penting dilakukan. Komentar dalam kuesener umumnya tidak diolah, padahal data tersebut mengandung informasi dalam mengungkap emosi mahasiswa dalam proses pembelajaran. Untuk itu deteksi dan klasifikasi emosi pada opini mahasiswa dapat memperbaiki hasil kuesioner. Penelitian ini bertujuan menerapkan metode klasifikasi emosi pada teks komentar mahasiswa berbasis pada leksikon emosi dari NRC Emolex. Jenis emosi yang akan dideteksi adalah 8 jenis emosi, yaitu marah (Anger), antisipasi (anticipation), jijik (disgust), takut (fear), bahagia (joy), sedih (sadness), terkejut (surprise) dan yakin (trust) . Data diambil dari komentar dan saran mahasiswa pada kuesioner pada Universitas AKPRIND Indonesia tahun 2020-2022 sebanyak 4000 data yang telah dilabeli secara manual. Tujuan lain dari studi ini adalah melihat sejauh mana efektivitas leksikon emosi Emolex untuk klasifikasi emosi teks kuesioner akademis. Hasil penelitian menunjukkan rata-rata akurasi sebesar 56,2%. Dari yang diketahui label emosinya 3 prosentase tertinggi ada pada label Sadness (19,2%), Joy(16,7%) dan Fear (13,5%) yang masing-masing memiliki akurasi 72%, 68% dan 68%. Dari penelitian terungkap bahwa kinerja Emolex untuk klasifikasi emosi masih kurang memuaskan dan memerlukan pengembangan leksikon lebih jauh lagi.

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Section: Pengumpulan Dataunclassified

Klasifikasi Emosi Berbasis Emolex dari Komentar Evaluasi Akademik Mahasiswa

Hamzah,

Ariyana

2024

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“…This study is based on textual emotion classification, where the goal of emotion classification, an extended field of sentiment analysis, is to assign possible emotions to a piece of text that most accurately reflect the mental state of the author. There are three ways to solve the emotion classification problem based on the approach: (1) binary emotion classification detects whether an emotion is present or not [7], (2) multi-class emotion classification classifies an instance into one of the predefined set of n labels [8], [9], (3) multi-label emotion classification classifies a given instance as ''neutral or no emotion'' or one or more from a set of predefined n labels that best represent the mental state of the author [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than Syntactic

Ameer

Bölücü

Sidorov³

et al. 2023

IEEE Access

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Social media is a widely used platform that provides a huge amount of user-generated content that can be processed to extract information about users' emotions. This has numerous benefits, such as understanding how individuals feel about certain news or events. It can be challenging to categorize emotions from text created on social media, especially when trying to identify several different emotions from a short text length, as in a multi-label classification problem. Most previous work on emotion classification has focused on deep neural networks such as Convolutional Neural Networks and Recurrent Neural Networks. However, none of these networks have used semantic and syntactic knowledge to classify multiple emotions from a text. In this study, semantic and syntactic aware graph attention networks were proposed to classify emotions from a text with multiple labels. We integrated semantic information in the graph attention network in the form of Universal Conceptual Cognitive Annotation and syntactic information in the form of dependency trees. Our extensive experimental results showed that our two models, UCCA-GAT (accuracy = 71.2) and Dep-GAT (accuracy = 68.7), were able to outperform the state-of-the-art performance on both the challenging SemEval-2018 E-c: Detecting Emotions (multi-label classification) English dataset (accuracy = 58.8) and GoEmotions dataset (accuracy = 65.9).

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Aspect-based Opinion Mining for Code-Mixed Restaurant Reviews in Indonesia

Suciati

Budi

2019

2019 International Conference on Asian Language Processing (IALP)

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An Ensemble-Classifier Based Approach for Multiclass Emotion Classification of Short Text

Cited by 4 publications

References 14 publications

Klasifikasi Emosi Berbasis Emolex dari Komentar Evaluasi Akademik Mahasiswa

Klasifikasi Emosi Berbasis Emolex dari Komentar Evaluasi Akademik Mahasiswa

Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than Syntactic

Aspect-based Opinion Mining for Code-Mixed Restaurant Reviews in Indonesia

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