Sentiment Analysis of People During Lockdown Period of COVID-19 Using SVM and Logistic Regression Analysis

Majumder, Sayan; Aich, Animikh; Das, Santanu

doi:10.2139/ssrn.3801039

Cited by 11 publications

(8 citation statements)

References 10 publications

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“…Basiri et al [6] analyzed coronavirus-related tweets from eight countries and obtained 85.5% accuracy. Highest 94.44% accuracy is achieved by the proposed model which is higher than the results obtained with existing techniques [10,31,22,14,6]. Confusion matrix of suggested model is depicted in Figure 4.5 (a).…”

Section: Experiments and Resultmentioning

confidence: 69%

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Ensemble Hybrid Model for COVID-19 Sentiment Analysis with Cuckoo Search Optimization Algorithm

Jain,

Kashyap

2023

SCPE

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The COVID-19 pandemic has caused anxiety and fear worldwide, affecting people's physical and mental health. This research work proposes a sentiment analysis approach to better understand the public's perception of COVID-19 in India. Two datasets are created by collecting tweets regarding COVID-19 in India. Pre-processing and analysis of datasets are performed by using natural language processing (NLP) techniques. Various features are extracted from collected tweets using three-word embeddings GloVe, fastText, Elmo. The optimal features are selected by cuckoo search optimization algorithm. Finally, the proposed hybrid model of Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM) is used to categorize the tweets into three sentiment categories. Proposed model achieved 94.44% accuracy, 90.34% precision, 88.53% sensitivity, and 89.53% F1 score. It significantly improved over previous approaches, which achieved 80% accuracy.

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Section: Experiments and Resultmentioning

confidence: 69%

“…Majumder et al analyzed the sentiments of Indian users tweets about COVID-19 from March 2020 to June 2020 [31]. They have used supervised machine learning-based support vector machine and Logistic Regression for sentiment analysis with accuracy rates of 91.50 % and 87.75%, respectively.…”

Section: Organization Of the Papermentioning

confidence: 99%

Ensemble Hybrid Model for COVID-19 Sentiment Analysis with Cuckoo Search Optimization Algorithm

Jain,

Kashyap

2023

SCPE

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“…Transitional findings are critical for decoding; global normalisation approaches and beam search have been used here. Logistic Regression has been implemented by Majumder et al [21], for identifying the sentiments of the people during the COVID pandemic. The researcher has also implemented the same using SVM.…”

Section: Literature Reviewmentioning

confidence: 99%

A Cloud Based Sentiment Analysis through Logistic Regression in AWS Platform

Sha¹

2023

Computer Systems Science and Engineering

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The use of Amazon Web Services is growing rapidly as more users are adopting the technology. It has various functionalities that can be used by large corporates and individuals as well. Sentiment analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related emotions. In this research work, sentiment analysis is performed on the AWS Elastic Compute Cloud (EC2) through Twitter data. The data is managed to the EC2 by using elastic load balancing. The collected data is subjected to preprocessing approaches to clean the data, and then machine learning-based logistic regression is employed to categorize the sentiments into positive and negative sentiments. High accuracy of 94.17% is obtained through the proposed machine learning model which is higher than the other models that are developed using the existing algorithms.

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“…Ada juga yang membandingkan SVM dan Logistic Regression dalam melakukan analisis sentimen [18]. Penelitian ini mengumpulkan data twitter orang-orang di seluruh India dan kemudian mengukur polaritasnya menggunakan NLP.…”

Section: A Analisis Sentimen Berbahasa Indonesiaunclassified

Analisis Sentimen Perkembangan Kasus Covid-19 Pada Komentar Facebook

Mashudi

Arief²

2021

JTIA

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Masyarakat banyak yang menggunakan sosial media sebagai sumber berita. Diantaranya, Facebook masih menjadi media sosial yang paling banyak digunakan masyarakat. Hal ini berlaku juga dengan berita terkait COVID-19. Sebagian besar masyarakat Indonesia mengikuti perkembangan berita tentang COVID-19 melalui Facebook. Dengan mengikuti di Facebook, masyarakat dapat menanggapi dan berkomentar terhadap isu-isu terrkait COVID-19. Saat ini, sudah banyak tanggapan yang dapat dilihat terkait COVID-19. Penelitian ini ditujukan untuk menganalisis sentimen komentar warganet terkait perkembangan kasus COVID-19. COVID-19 masih akan berada di tengah masyarakat dalam waktu lama. Karena itu, kewaspadaan masyarakat juga perlu dijaga. Hal ini dilakukan agar dapat mempercepat berakhirnya pandemi ini. Tingkat kewaspadaan itu salah satunya tercermin dari komentar masyarakat terhadap perkembangan COVID-19. Komentar yang ada dapat dilakukan analisis sentimen yang mencerminkan kewaspadaan masyarakat terhadap kasus COVID-19. Empat model telah dibuat untuk melakukan hal ini. Hasilnya, keempat model menghasilkan analisis sentimen yang berbeda-beda. Model Naïve Bayes dengan tingkat akurasi tertinggi lebih banyak mendeteksi sentimen negatif. Sebaliknya, model Random Forest yang lebih netral lebih banyak mendeteksi sentimen positif. Hasil klasifikasi ini dapat digunakan pemerintah dalam mengambil kebijakan terkait COVID-19

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Sentiment Analysis of People During Lockdown Period of COVID-19 Using SVM and Logistic Regression Analysis

Cited by 11 publications

References 10 publications

Ensemble Hybrid Model for COVID-19 Sentiment Analysis with Cuckoo Search Optimization Algorithm

Ensemble Hybrid Model for COVID-19 Sentiment Analysis with Cuckoo Search Optimization Algorithm

A Cloud Based Sentiment Analysis through Logistic Regression in AWS Platform

Analisis Sentimen Perkembangan Kasus Covid-19 Pada Komentar Facebook

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