Analisis Sentimen Pada Tweet Tentang Penanganan Covid-19 Menggunakan Word Embedding Pada Algoritma Support Vector Machine Dan K-Nearest Neighbor

Sabrila, Trifebi Shina; Sari, Veronica Retno; Minarno, Agus Eko

doi:10.21111/fij.v6i2.5536

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…Mengidentifikasi teks berdasarkan suara manusia banyak dipengaruhi oleh variasi dan karakteristik unik dari pembicara, dimana dalam tahapan ini setelah dilakukan konversi suara, teks hasil konversi diperggunakan sebagai Data Collection yang akan diektraksi sebelum dilakukan analisa sentimen. Fungsional dari data teks secara tidak langsung memerlukan mekanisme ekstraksi untuk memperoleh nilai masing-masing fitur yang terdapat didalam data input [11], mekanisme ekstraksi fitur juga berperan penting dalam proses sentiment analysis sehingga berpengaruh terhadap hasil klasifikasi [12].…”

Section: Ekstrasi Fiturunclassified

Implementasi Convolution-Augmented Transfomer Berbasis Kecerdasan Buatan dalam Analisis Sentimen Teks Hasil Konversi Suara ke Teks

Utomo,

Wahyudi,

Nalendra

2024

generation

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Keterbatasan mendapatkan informasi yang dialami penyandang disabilitas menjadikan mereka kurang update terhadap perkembangan yang terjadi sehingga secara tidak langsung mendorong berbagai upaya untuk mendapatkan informasi tanpa mempedulikan sumber dan konteks informasi yang diperoleh, konteks informasi yang diperoleh dalam hal ini berkaitan dengan emosi yang berusaha disampaikan lewat tulisan atau teks informasi, maka perlu dirancang dan diimplementasikan yang mampu mengekstraksi dan menemukan inteprestasi emosi bermuatan positif, negatif atau netral dari teks hasil konversi teknologi Speech to Text, sehingga dapat membantu penderita disabilitas pendengaran dalam memahami konteks dan emosi yang terkandung didalam informasi. Aplikasi Speech to Text yang dikombinasikan dengan metode Conformer berbasis kecerdasan buatan dapat membantu penyandang disabilitas pendengaran untuk memahami sentimen atau emosi dari teks hasil konversi suara. Dengan menggunakan kecerdasan buatan yang tergabung dalam metode Conformer dapat dilakukan klasifikasi sentimen terhadap teks hasil konversi juga dapat dideteksi topik yang disampaikan, sehingga diharapkan dapat dimanfaatkan penyandang disabilitas pendengaran dalam memberikan umpan balik yang tidak menyinggung perasaan dan sesuai topik bahasan.

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Section: Ekstrasi Fiturunclassified

Implementasi Convolution-Augmented Transfomer Berbasis Kecerdasan Buatan dalam Analisis Sentimen Teks Hasil Konversi Suara ke Teks

Utomo,

Wahyudi,

Nalendra

2024

generation

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“…Yao et al [16] and other authors used advanced machine language algorithms to detect peoples' interaction from the vaccinations [17] [18]. Support Vector Machine has been used from different authors to measure the sentiment analysis, for example Hayati et al [19] and Sabrila et al [20] used the Support Vector Machine algorithm as well as K-Nearest Neighbor Algorithm.…”

Section: Sentiment Analysis Based On Social Media Posts During Covid-19mentioning

confidence: 99%

Machine Learning Chatbot for Sentiment Analysis of Covid-19 Tweets

Assayed¹,

Shaalan²,

Alkhatib³

et al. 2023

Computer Networks &Amp; Communications

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The various types of social media were increased rapidly, as people’s need to share knowledge between others. In fact, there are various types of social media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter remains one of the most popular social application that people use for sharing their emotional states. However, this has increased particularly during the COVID-19 pandemic. In this paper, we proposed a chatbot for evaluating the sentiment analysis by using machine learning algorithms. The authors used a dataset of tweets from Kaggle’s website, and that includes 41157 tweets that are related to the COVID-19. These tweets were classified and labelled to four categories: Extremely positive, positive, neutral, negative, and extremely negative. In this study, we applied Machine Learning algorithms, Support Vector Machines (SVM), and the Naïve Bayes (NB) algorithms and accordingly, we compared the accuracy between them. In addition to that, the classifiers were evaluated and compared after changing the test split ratio. The result shows that the accuracy performance of SVM algorithm is better than Naïve Bayes algorithm, even though Naïve Bayes perform poorly with low accuracy, but it trained the data faster comparing to SVM.

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“…Furthermore, research by (Sabrila et al, 2021) conducted a sentiment analysis of the handling Covid-19 by the Indonesian government. The methods used Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) by using word embedding feature extraction to compare the performance of the two methods.…”

Section: Related Studiesmentioning

confidence: 99%

Sentiment Analysis on Twitter Social Media towards Shopee E-Commerce through Support Vector Machine (SVM) Method

Hutapea,

Maharani

2023

jinav j. inf. vis.

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Shopee is e-commerce widely accessed and used in this era. Many people use Shopee because the products offered are cheaper and more affordable. Despite the fact that Shopee is a well-known e-commerce, it still requires responses and suggestions from the public to maintain or improve the features required. In this study, public sentiment analysis was carried out on Twitter social media related to the Shopee marketplace. This study collected data that contained tweets from predetermined keywords and used Word2Vec and Support Vector Machine classification methods. The use of Word2Vec influenced the level of accuracy so that it increased for each SVM kernel. Meanwhile, the best hyperparameter tuning was found in the polynomial kernel, with an accuracy rate of 93.20%.

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Analisis Sentimen Pada Tweet Tentang Penanganan Covid-19 Menggunakan Word Embedding Pada Algoritma Support Vector Machine Dan K-Nearest Neighbor

Cited by 7 publications

References 14 publications

Implementasi Convolution-Augmented Transfomer Berbasis Kecerdasan Buatan dalam Analisis Sentimen Teks Hasil Konversi Suara ke Teks

Implementasi Convolution-Augmented Transfomer Berbasis Kecerdasan Buatan dalam Analisis Sentimen Teks Hasil Konversi Suara ke Teks

Machine Learning Chatbot for Sentiment Analysis of Covid-19 Tweets

Sentiment Analysis on Twitter Social Media towards Shopee E-Commerce through Support Vector Machine (SVM) Method

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