| Resumen |
Although over 169 million people in the world are familiar with the Urdu language and a large quantity of Urdu data is being generated on different social websites daily, very few research studies and efforts have been completed to build language resources for the Urdu language and examine user sentiments. The primary objective of this study is twofold: (1) develop a benchmark dataset for resource-deprived Urdu language for sentiment analysis and (2) evaluate various machine and deep learning algorithms for sentiment. To find the best technique, we compare two modes of text representation: count-based, where the text is represented using word n -gram feature vectors and the second one is based on fastText pre-trained word embeddings for Urdu. We consider a set of machine learning classifiers (RF, NB, SVM, AdaBoost, MLP, LR) and deep leaning classifiers (1D-CNN and LSTM) to run the experiments for all the feature types. Our study shows that the combination of word n -gram features with LR outperformed other classifiers for sentiment analysis task, obtaining the highest F1 score of 82.05% using combination of features. © 2013 IEEE. |
