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Analysis of Ghanaian Political Sentiments using Deep Learning and Machine Learning Approaches

Domaine:

natural language processing

Type de record:

paper
Créateur:
Charles Jnr. AsieduQian CaoXiulan Hao
Éditeur:
Zenodo
Hôte:avatar

Over the past decade, with the growth of social media platforms such as Twitter, Instagram, and Facebook, the volume of data gathered from users worldwide has increased substantially, leading to the publishing and collecting of micro-opinion pieces. These help people modify or document their feelings about objects, individuals, and events. Most political parties in Africa use the Twitter platform to communicate. In this paper, an attempt is made to mine tweets, capture political sentiments, and analyze it by performing deep learning and machine learning models on the tweets. Specific hashtags are used to aid the collection of tweets. The extraction of tweets about the Ghanaian Government and Elections is carried out along with the study of sentiments among Ghanaian Twitter users towards the current government of Ghana to predict its faith in the next general elections. Subsequently, the performance of the deep learning and machine learning approaches will be evaluated.

Visit

doi.org

Tasks

sentiment analysistext classification

Tags

Twitter, tweets, sentiments, SVM, Naïve Bayes, LSTM

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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