Sentiment Analysis of Balochi Text Using Deep Learning
DOI:
https://doi.org/10.21015/vtcs.v13i1.2081Abstract
Balochi is a low-resource language with limited available data for computational modelling. This study aims to perform sentiment analysis on Balochi text using machine learning techniques. To address the scarcity of linguistic data, we contribute a large, newly constructed dataset of Balochi text. Our proposed model incorporates feature extraction and data augmentation within deep learning algorithms to classify sentiments as positive, negative, or neutral. We evaluate both traditional machine learning methods—such as Random Forest and Support Vector Machine (SVM)—and advanced deep learning models, including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).The experimental evidence proves that LSTM and GRU are more effective than traditional methods, and the accuracy rates of sentiment classification with their help are 83.57% and 81.23%, respectively. It has been experimentally verified that, when it comes to the Balochi sentiment analysis, deep learning methods can be more effective than the traditional ones.
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