Polarity classification of political tweets in Mexico using a lexicon-based approach

Paredes-García, Wilfrido J.; Gómez‐Loenzo, Roberto A.

doi:10.1109/coniin.2018.8489802

Cited by 3 publications

References 13 publications

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Social Media Profiling for Political Affiliation Detection

Khan,

Khan

2024

Hum-Cent Intell Syst

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The notion of discerning political affiliations from users’ social media behavior instills a sense of unease in many. Democracy necessitates that individuals’ political affiliations remain private, and social media challenges this foundational principle of democracy. This study uses BERT, a pre-trained language model to analyze X’s (formally Twitter) users and their political affiliations to understand that how much it is easy now to find the political affiliation of people. We collect posts in both English and Urdu languages from different political leaders and their followers, which are used to fine-tune the BERT model. The model classifies the users’ profiles into Pro, Neutral, or Anti-government classes. To assess the performance of the proposed method, experiments are conducted to evaluate its accuracy, efficiency, and effectiveness. The findings of this study confirm the hypothesis that it is easy to detect the political affiliation of individuals using social media with high accuracy (69% for English and 94% for Urdu language) and it can undermine democracy.

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Social Media Profiling for Political Affiliation Detection

Khan,

Khan

2024

Hum-Cent Intell Syst

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Social media, AI, and political affiliation

Bukhari

Khan

et al. 2021

2021 International Conference on Innovative Computing (ICIC)

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Sentiment Analysis on Covid-19 Vaccination Using Machine Learning Techniques

Bhalerao¹,

Naiknaware²,

Manza³

et al. 2023

Advances in Computer Science Research

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In this research work, we have performed Machine Learning and Lexicon Based Techniques to identify and analyze user's expression or opinion on covid-19 vaccination from social media platform that is Twitter and acquainted the bulk tweets from 01 June 2021 to August 2021 using various twitter hashtags. Machine learning based Classifiers are used for investigating the evaluation performance of Algorithms. Real time datasets and machine Learning Algorithms are compared with Best Data classification Evaluation based on the size of train data also another approach is to investigating the polarity by using Lexicon Based approach for this Bing Liu Lexicons and Stanford University Lexicons are used. The global pandemic has created the medical emergency and stops the many regular activities. The whole world in the lockdown or quarantine to because Coronavirus disease. Among them, Covaxin, Covishield, Pfizer, Moderna and SputnikVare popular. Universally publicare articulating opinions on protection and success of the vaccines on social media. Research article shows, such tweets are collected from developer Application Management using a Twitter API. Unprocessed tweets are kept and preprocessed through Machine Learning techniques. Users opinion are predicted using a Classifiers Decision Tree, Support Vector Machine, K NN Algorithm and Naïve Bayes. Comparative machine learning classifiers study here comparative analysis is got highest accuracy of 97% for Decision tree with Covaxin dataset, Support vector machine with 94% for SputnikV, Naïve Bayes got highest accuracy of 95 for Covishield dataset and KNN got Highest accuracy of 96% for Covaxin. The Lexicon Based polarity classifies the score into three users opinions, positive, negative, and neutral. Result shows that, Covaxin shows 28.

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Polarity classification of political tweets in Mexico using a lexicon-based approach

Cited by 3 publications

References 13 publications

Social Media Profiling for Political Affiliation Detection

Social Media Profiling for Political Affiliation Detection

Social media, AI, and political affiliation

Sentiment Analysis on Covid-19 Vaccination Using Machine Learning Techniques

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