Deep Learning Technique of Sentiment Analysis for Twitter Database

Gattan, Atif M.

doi:10.3991/ijim.v16i01.27575

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…This study presents a system that uses CNN to determine the species of butterfly. Deep learning image processing is used instead of using standard image processing because standard image processing is not powerful enough to classify the butterfly species [24]. CNN consists of a few structural layers [25].…”

Section: Deep Learning Image Processmentioning

confidence: 99%

Biological Butterfly Characterization with Mobile System Using Convolutional Neural Network (CNN) Classify Image

Saedan,

Kassim,

Abd Aziz

2024

Int. J. Interact. Mob. Technol.

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This study presents the development of a mobile identification system that detects biological butterfly characteristics through deep learning by capturing images. The challenge identified is that butterfly identification and recognition are difficult tasks because there are too many species, and it is hard to classify the types of butterfly species. Butterflies are also difficult to differentiate from each other, and limited studies are done using computer database referrals for butterflies’ characterization. This study aims to develop an automated computer program to easily identify the species of butterflies. Deep learning in image processing is programmed, which can control the qualification, segmentation, and classification of images and automatically detect butterfly characterization. The design system consists of three stages: capture, feature extraction, and butterfly recognition. Then, multiple recognition clues such as shape, color, texture, and size are extracted and analyzed to analyze and recognize the butterfly. This approach is faster and less complex than the previous approach. The result successfully presents a convolutional neural network (CNN) to classify images after training and characterization. The graphics processing unit (GPU) that trains the image dataset presents 86% image accuracy in the allocated time. This research is significant in such a way that new butterfly species will be automatically collected and stored on the online server. The information could be treasured as a valuable butterfly database.

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Section: Deep Learning Image Processmentioning

confidence: 99%

Biological Butterfly Characterization with Mobile System Using Convolutional Neural Network (CNN) Classify Image

Saedan,

Kassim,

Abd Aziz

2024

Int. J. Interact. Mob. Technol.

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“…The information is investigated with the assistance of API to classify outcomes as positive, negative, or neutral. For understanding the mentality of people sentiment analysis can be used [21]. In teaching-learning process VLE delivers a set of communication and interaction tools utilized by learners and educators.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensemble Machine Learning Model for University Students’ Risk Prediction and Assessment of Cognitive Learning Outcomes

Mary.T¹,

J²

2023

IJIET

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One of the biggest challenges in higher educational institutions is to avoid students’ failures. Globally student dropout is a serious issue. Risk of dropouts can be identified at an earlier stage using machine learning classifiers, as they have gained more popularity in both academia and industry. The research team suggests that early prediction facilitates educators and higher education administrators to take necessary measures to prevent dropouts. Data for the research were collected from 530 Indian students when they were engaged in online learning during pandemic crisis. This research work involves two phases. In first phase, hybrid ensemble strategy is focused that integrates two powerful machine learning algorithms namely Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) for early at-risk prediction. The result is a fast procedure for classification of at-risk students which is competitive in accuracy and highly robust. Prediction models are developed using ensemble learning, furthermore ensemble models are combined into a single meta-model, which provides best outcomes to enable higher education institutions for predictive analysis. Moreover, it correctly classified students’ at-risk regarding accuracy, precision, recall and F1-score with values of 93%, 91.52%, 96.42% and 93.91% respectively. In second phase, prediction model is deployed by creating a web application using. Net framework to sense students’ sentiments using Azure cognitive services text analytics (Application Programming Interface) API for detecting cognitive behavioral outcomes in online learning environment.

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“…Penelitian tersebut menggunakan algoritme Deep Learning dengan tahapan Preprocessing, Dense Embedding, Hidden Layer dan klasifikasi sentimen. Hasil penelitian ini menunjukkan bahwa teknik Deep Learning dapat digunakan untuk analisis sentimen pada data Twitter terutama pada data yang jumlahnya sangat banyak [12]. B. Dasar Teori K-Nearest Neighbors atau yang disingkat dengan KNN merupakan salah satu metode klasifikasi data yang sering dipakai.…”

Section: A Penelitian Sebelumnyaunclassified

Analisis Sentimen Sepak Bola Indonesia pada Twitter menggunakan K-Nearest Neighbors dan Random Forest

Prabowo¹,

Sudianto²

2023

JSAI

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Twitter merupakan salah satu media sosial yang paling banyak digunakan saat ini. Twitter memungkinkan pengguna untuk memberikan berita dan komentar terbaru tentang peristiwa yang sedang berlangsung di Dunia. Di Indonesia, laga final piala AFF Suzuki Cup 2020 menjadi perbincangan hangat karena untuk keenam kalinya Indonesia menjadi runner-up setelah tahun 2000, 2002, 2004, 2010, dan 2016 penampilan Timnas Indonesia. Dengan banyaknya opini dan kritik yang beredar, membedakan opini positif dan negatif membutuhkan waktu yang lama. Oleh karena itu, diperlukan suatu model analisis sentimen yang dapat mengklasifikasikan opini positif dan negatif sebagai bahan evaluasi bagi Timnas Indonesia di masa yang akan datang. Pada penelitian ini, klasifikasi analisis sentimen menggunakan metode algoritme K-Nearest Neighbors dan Random Forest. Data yang digunakan berasal dari balasan kicauan akun Twitter Joko Widodo terkait ucapan selamat kepada Timnas Indonesia usai bertanding melawan Thailand di AFF Suzuki Cup 2020. Berdasarkan hasil pengujian, akurasi algoritme K-Nearest Neighbors 75% lebih baik daripada algoritme Random Forest dengan akurasi 71%

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Deep Learning Technique of Sentiment Analysis for Twitter Database

Cited by 4 publications

References 11 publications

Biological Butterfly Characterization with Mobile System Using Convolutional Neural Network (CNN) Classify Image

Biological Butterfly Characterization with Mobile System Using Convolutional Neural Network (CNN) Classify Image

Ensemble Machine Learning Model for University Students’ Risk Prediction and Assessment of Cognitive Learning Outcomes

Analisis Sentimen Sepak Bola Indonesia pada Twitter menggunakan K-Nearest Neighbors dan Random Forest

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