Sentiment Analysis From Urdu Language-based Text using Deep Learning Techniques.
International Conference on Advancements in Computational Sciences(2024)
摘要
The advancement in technology has enabled people to give reviews in their native language. Urdu is spoken by millions of people worldwide and a substantial amount of textual data is generated in the Urdu language. Therefore, there is a need to explore Urdu language-based data to get valuable insights into public opinion in Urdu-speaking communities. In the past, machine learning, lexicon-based, and rule-based techniques have been employed in sentiment analysis. Recently, sentiments have been classified by using techniques based on transformers due to the integration of self-attention mechanisms. In this work, a framework based on deep learning techniques for sentiment analysis of Urdu language is presented that comprises data curation, pre-processing, and classification stages. We have used a publicly available IMDB dataset of movie reviews translated into Urdu. For sentiment classification, we have performed experiments using three deep learning models i.e., 1-dimensional convolutional neural network (1D-CNN), long short-term memory (LSTM), and Multilingual-MiniLM-L12-H384 transformer: Our experimental results show that the transformer architecture is well-suited for Urdu language sentiment classification and attained a significant accuracy of $89.36 \%$.
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关键词
sentiment analysis,urdu language,deep learning models,transformer,word embedding
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