Sentiment analysis on Twitter using the combination of lexicon-based and support vector machine for assessing the performance of a television program

Tiara, Tiara; Sabariah, Mira Kania; Effendy, Veronikha

doi:10.1109/icoict.2015.7231456

Cited by 16 publications

(4 citation statements)

References 4 publications

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“…For the 2016 US presidential elections, Tiara et al [19] conducted a sentiment study on Twitter. They calculated sentiment using two approaches: lexicon-based sentiment analysis using Opinion Finder and sentiment analysis with machine learning using the natural language processing toolkit (NLTK) to implement the algorithm Naive Bayes (NB).…”

Section: Related Workmentioning

confidence: 99%

Sentiment analysis using global vector and long short-term memory

Kusum

Panda

2022

IJEECS

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Tweet sentiment analysis is a Deep Learning study that is beneficial for automatically determining public opinion on a certain topic. Using the Long Short-Term Memory (LSTM) algorithm, this paper aims to proposes a Twitter analysis technique that divides Tweets into two categories (positive and negative). The Global Vector (GloVe) word embedding score is used to rate many selected words as network input. GloVe converts words into vectors by building a corpus matrix. The GloVe outperforms its prior model, owing to its smaller vector and corpora sizes. GloVe has a higher accuracy than the model word embedding word2vec, Continuous Bag of Word(CBoW), and word2vec Skip-gram. The preprocessed term variation was conducted to test the performance of sentiment classification. The test results show that this proposed method has succeeded in classifying with the best results with an accuracy of 95.61%.

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Section: Related Workmentioning

confidence: 99%

Sentiment analysis using global vector and long short-term memory

Kusum

Panda

2022

IJEECS

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“…Sebuah matriks dari prediksi akan dibandingkan dengan kelas asli yang berisi informasi aktual dan prediksi nilai klasifikasi. Tabel 2. menunjukkan confusion matrix yang digunakan untuk membantu dalam perhitungan sistem evaluasi (Tiara, Sabariah, & Effendy, 2015). Berikut adalah struktur confusion matrix yang dapat dilihat pada Tabel 2. serta rumus perhitungan nilai accuracy, sensitivity dan specificity berdasarkan Persamaan (4), Persamaan (5), dan Persamaan (6).…”

Section: Confusion Matrixunclassified

Klasifikasi Persepsi Pengguna Twitter Terhadap Tuntutan Keringanan Pembayaran Uang Kuliah Tunggal (Ukt) Pada Masa Pandemi Covid-19 Menggunakan K-Nearest Neighbor

Riefky¹,

Anandyani²

2021

semnasoffstat

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Virus COVID-19 telah menyebar ke seluruh dunia sehingga berdampak terhadap berbagai sektor. Salah satunya adalah sektor pendidikan. Sektor pendidikan di Indonesia menerapkan belajar dari rumah yang mengakibatkan dunia pendidikan belum sepenuhnya kembali sehingga tak lepas dari permasalahan biaya UKT yang masih berjalan ditambah dengan biaya kuota internet yang mahal untuk kuliah online. Tujuan dari penelitian ini adalah mendapatkan hasil ketepatan klasifikasi persepsi pengguna twitter terhadap tuntutan keringanan pembayaran UKT pada masa pandemi COVID-19 menggunakan k-nearest neighbor. Data yang digunakan pada penelitian ini berasal dari data pengguna media sosial Twitter yang sering menggunakan hashtag “Mendikbud dicari Mahasiswa” yang sudah dilakukan data filtering dan cleansing sebesar 2768 tweets dengan partisi data sebanyak 70% untuk data training dan 30% untuk data testing. Kesimpulan yang dapat dikemukakan pada penelitian ini adalah netizen twitter memiliki persepsi negatif tentang tuntutan keringanan pembayaran UKT pada masa pandemi COVID-19. Netizen twitter yang berpendapat tentang tuntutan keringanan pembayaran UKT pada masa pandemi COVID-19 lebih banyak menggunakan kata “mendikbuddicarimahasiswa”, “kampus” dan “ukt” dalam satu kali tweet. Nilai k (kelompok) sebanyak 2 merupakan nilai k yang optimum dengan nilai akurasi sebesar 83,25%.

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“…Their findings indicated that only a small percentage (approximately 22%) of topics discussed during these visits focused on mental health. The researchers emphasized the need for systematic interventions and guidelines to address mental health issues during doctor visits for elderly individuals [15]. However, the limitation of this solution lies in the fact that elders may not freely discuss their mental well-being in such settings, which raises concerns about the effectiveness of identifying and addressing mental health issues solely through doctor visits [12].…”

Section: Introductionmentioning

confidence: 99%

An Emotion Detection Mobile Applicationto Decrease Potential Mental Disorder Among Seniorsusing Sentiment Analysis from Audio Data

Qiu¹,

Carter²

2023

Advanced Computer Science and Information Technology Trends

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We wanted to create a better, more trustworthy solution for caretakers to determine the emotion and feelings their patients are experiencing. This is useful for caretakers to adjust how they go about caring for their patients. To solve this, we decided to create a mobile consumer application. Architecturally, the user (caretaker) will be using this mobile app as a front end which will record and send audio of the patient to a backend service. The service will be hosted on AWS and it will be a sentiment analysis algorithm written in Python. The service will analyze and determine the feeling of the patient and send it back to the front end for the caretaker to see. The front end was written in Flutter. To evaluate the effectiveness of the proposed mobile application, we conducted testing focused on two key aspects: accuracy and response speed. The accuracy of the sentiment analysis algorithm was assessed by comparing the analysis results of pre-recorded audio with predetermined feelings. This evaluation aimed to measure the algorithm's ability to correctly identify and classify emotions. Additionally, we tested the speed of response by sending audio samples of varying lengths, ranging from 2 seconds to 60 seconds. The objective was to determine the optimal response time for providing feedback on the user's mood. Our findings revealed that the algorithm demonstrated high accuracy in detecting emotions within the tested audio samples. Moreover, the ideal response time for generating feedback was identified as 5 seconds, striking a balance between promptness and accuracy. These testing results validate the efficacy of the proposed application in accurately analyzing sentiment and providing timely responses, supporting its potential as an effective tool for monitoring and addressing the mental well-being of elderly individuals. This proposed mobile app, utilizing sentiment analysis, offers a convenient and accurate means of monitoring and addressing the mental well-being of elderly individuals. Its potential to combat loneliness and provide timely support makes it a valuable tool for improving their overall quality of life.

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Sentiment analysis on Twitter using the combination of lexicon-based and support vector machine for assessing the performance of a television program

Cited by 16 publications

References 4 publications

Sentiment analysis using global vector and long short-term memory

Sentiment analysis using global vector and long short-term memory

Klasifikasi Persepsi Pengguna Twitter Terhadap Tuntutan Keringanan Pembayaran Uang Kuliah Tunggal (Ukt) Pada Masa Pandemi Covid-19 Menggunakan K-Nearest Neighbor

An Emotion Detection Mobile Applicationto Decrease Potential Mental Disorder Among Seniorsusing Sentiment Analysis from Audio Data

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