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

<p>Worldwide websites publish enormous amounts of text, audio, and video content every day. This valuable information allows for the assessment of regional trends and general public opinion. Based on consumers’ online behavioural habits, businesses are showing them their chosen ads. It is difficult to carefully analyse these raw data to find valuable trends, especially for a language with limited resources like Urdu. There have not been many studies or efforts to create language resources for the Urdu language and analyse people’s sentiment, even though there are more than 169 million Urdu speakers in the world and a sizable amount of Urdu data is generated on various social media platforms every day. However, there has been relatively little research on sentiment analysis in Urdu. Researchers have primarily performed studies in English and Chinese. In response to this gap, we suggest an emotion analyser for Urdu, the primary language of Asia, in this research study. In this paper, we propose to assess sentiment in Urdu review texts by integrating a bidirectional long short-term memory (BiLSTM) model with a convolutional neural network (CNN). We contrast the CNN, LSTM, BiLSTM, and CNN-LSTM models with the CNN-BiLSTM model. With an accuracy rate of 0.99 %, the CNN-BiLSTM model performed better than the other models in an initial investigation.</p>
</abstract>

Worldwide websites publish enormous amounts of text, audio, and video content every day. This valuable information allows for the assessment of regional trends and general public opinion. Based on consumers’ online behavioural habits, businesses are showing them their chosen ads. It is difficult to carefully analyse these raw data to find valuable trends, especially for a language with limited resources like Urdu. There have not been many studies or efforts to create language resources for the Urdu language and analyse people’s sentiment, even though there are more than 169 million Urdu speakers in the world and a sizable amount of Urdu data is generated on various social media platforms every day. However, there has been relatively little research on sentiment analysis in Urdu. Researchers have primarily performed studies in English and Chinese. In response to this gap, we suggest an emotion analyser for Urdu, the primary language of Asia, in this research study. In this paper, we propose to assess sentiment in Urdu review texts by integrating a bidirectional long short-term memory (BiLSTM) model with a convolutional neural network (CNN). We contrast the CNN, LSTM, BiLSTM, and CNN-LSTM models with the CNN-BiLSTM model. With an accuracy rate of 0.99 %, the CNN-BiLSTM model performed better than the other models in an initial investigation.

Sentiment Analysis Based on Urdu Reviews Using Hybrid Deep Learning Models

Madan Mohan Malaviya University of Technology

Applied Computer Systems

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

{"article-title":"Sentiment Analysis Based on Urdu Reviews Using Hybrid Deep Learning Models"}

Worldwide websites publish enormous amounts of text, audio, and video content every day. This valuable information allows for the assessment of regional trends...

Sentiment Analysis Based on Urdu Reviews Using Hybrid Deep Learning Models

Publié en ligne: 29 janv. 2024

Pages: 258 - 265

DOI: https://doi.org/10.2478/acss-2023-0026

Mots clés
Emotion analyser, people sentiment, public opinion, sentiment analysis, Urdu review

© 2023 Neha Singh et al., published by Sciendo

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

Sentiment Analysis Based on Urdu Reviews Using Hybrid Deep Learning Models

Publié en ligne: 29 janv. 2024

Pages: 258 - 265

DOI: https://doi.org/10.2478/acss-2023-0026

Mots clésEmotion analyser, people sentiment, public opinion, sentiment analysis, Urdu review

© 2023 Neha Singh et al., published by Sciendo

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

Mots clés
Emotion analyser, people sentiment, public opinion, sentiment analysis, Urdu review