The Exploring Political Emotions Sentiment Analysis of Urdu Tweets

Ehtisham Ur Rehman, Muhammad Nouman Khan, Najam Aziz
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Ehtisham Ur Rehman, Muhammad Nouman Khan, Najam Aziz: Department of Computer ScienceUET, Peshawar, Pakistan. Department of Computer SystemsEngineering UET Peshawar, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 5, 296-311

Abstract: This research is a multi-text categorization based on a collection of Pakistani political texts. The major goal of this research is to use Natural Language Processing (NLP) and Machine Learning classification models to categorize multi-text for Urdu. Political tweets from 13 different Pakistani famous leaders were collected for this research. These politicians make use of the platform to promote themselves and engage with their supporters. To analyze the model accuracy the desired dataset is divided into six categories which have been composed of their official Twitter account. We also collect top trends from Pakistan and around the world to examine current trends regularly. In the proposed research, the major political corpus data comprises 1300+ tweets in the Urdu language, encompassing political policies, campaigns, opinions, and so on. Sentiment analysis is an essential component of every deep learning approach. For that, we have used the deep learning approach i.e. sentiment analysis of the politician since it provides insight into their moods and views on a certain topic. Furthermore, text corpus pre-processing is conducted utilizing NLP techniques, such as data cleaning, data balancing, and stop word removal. TF-IDF is used as word filtering for feature extraction count vectors. Machine Learning classification algorithms such as SVM, Decision Tree, XGboost, and Random Forest, and for implementation of neural network we have used Word2vector.

Keywords: Political Emotions; Sentiment Analysis; Exploring; Urdu Tweets; Social Media; Political Discourse; Emotion Detection; Computational Linguistics; Urdu Language Processing (search for similar items in EconPapers)
Date: 2024
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