<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The world is heading towards more modernized and digitalized data and therefore a significant growth is observed in the active number of social media users with each passing day. Each post and comment can give an insight into valuable information about a certain topic or issue, a product or a brand, etc. Similarly, the process to uncover the underlying information from the opinion that a person keeps about any entity is called a sentiment analysis. The analysis can be carried out through two main approaches, i.e., either lexicon-based or machine learning algorithms. A significant amount of work in the different domains has been done in numerous languages for sentiment analysis, but minimal research has been conducted on the national language of Pakistan, which is Urdu. Twitter users who are familiar with Urdu update the tweets in two different textual formats either in Urdu Script (Nastaleeq) or in Roman Urdu. Thus, the paper is an attempt to perform the sentiment analysis on the Urdu language by extracting the tweets (Nastaleeq and Roman Urdu both) from Twitter using Tweepy API. A machine learning-based approach has been adopted for this study and the tool opted for the purpose is WEKA. The best algorithm was identified based on evaluation metrics, which comprise the number of correctly and incorrectly classified instances, accuracy, precision, and recall. SMO was found to be the most suitable machine learning algorithm for performing the sentiment analysis on Urdu (Nastaleeq) tweets, while the Roman Urdu Random Forest algorithm was identified as the best one.</p>
</abstract>

The world is heading towards more modernized and digitalized data and therefore a significant growth is observed in the active number of social media users with each passing day. Each post and comment can give an insight into valuable information about a certain topic or issue, a product or a brand, etc. Similarly, the process to uncover the underlying information from the opinion that a person keeps about any entity is called a sentiment analysis. The analysis can be carried out through two main approaches, i.e., either lexicon-based or machine learning algorithms. A significant amount of work in the different domains has been done in numerous languages for sentiment analysis, but minimal research has been conducted on the national language of Pakistan, which is Urdu. Twitter users who are familiar with Urdu update the tweets in two different textual formats either in Urdu Script (Nastaleeq) or in Roman Urdu. Thus, the paper is an attempt to perform the sentiment analysis on the Urdu language by extracting the tweets (Nastaleeq and Roman Urdu both) from Twitter using Tweepy API. A machine learning-based approach has been adopted for this study and the tool opted for the purpose is WEKA. The best algorithm was identified based on evaluation metrics, which comprise the number of correctly and incorrectly classified instances, accuracy, precision, and recall. SMO was found to be the most suitable machine learning algorithm for performing the sentiment analysis on Urdu (Nastaleeq) tweets, while the Roman Urdu Random Forest algorithm was identified as the best one.

Urdu Sentiment Analysis

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Urdu Sentiment Analysis"}

The world is heading towards more modernized and digitalized data and therefore a significant growth is observed in the active number of social media users...

Urdu Sentiment Analysis

Data publikacji: 23 sie 2022

Zakres stron: 30 - 42

DOI: https://doi.org/10.2478/acss-2022-0004

Słowa kluczowe
Machine learning algorithms, sentiment analysis, Tweepy, WEKA

© 2022 Iffraah Rehman et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Urdu Sentiment Analysis

Data publikacji: 23 sie 2022

Zakres stron: 30 - 42

DOI: https://doi.org/10.2478/acss-2022-0004

Słowa kluczoweMachine learning algorithms, sentiment analysis, Tweepy, WEKA

© 2022 Iffraah Rehman et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Machine learning algorithms, sentiment analysis, Tweepy, WEKA