Sentiment Analysis of Balochi Text Using Deep Learning
Shumaila Hussain, Sibghat Ullah Bazaib, Shahab Qadir, Shah Marjan, Muhammad Imran Ghafoor, Paras Pervaiz · VAWKUM Transactions on Computer Sciences · 2025
Balochi is a low-resource language with limited available data for computational modeling. 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). Experimental results demonstrate that LSTM and GRU outperform conventional approaches, achieving sentiment classification accuracy rates of 83.57% and 81.23 %, respectively. Experimental evidence confirms that deep learning techniques outperform conventional approaches in Balochi sentiment analysis