Lexical Variation and Sentiment Analysis of Roman Urdu Sentences with Deep Neural Networks

Sentiment analysis is the computational study of re-views, emotions, and sentiments expressed in the text. In the past several years, sentimental analysis has attracted many concerns from industry and academia. Deep neural networks have achieved significant results in sentiment analysis. Current methods mainly focus on the English language, but for minority languages, such as Roman Urdu that has more complex syntax and numerous lexical variations, few research is carried out on it. In this paper, for sentiment analysis of Roman Urdu, the novel “Self-attention Bidirectional LSTM (SA-BiLSTM)” network is proposed to deal with the sentence structure and inconsistent manner of text representation. This network addresses the limitation of the unidirectional nature of the conventional architecture. In SA-BiLSTM, Self-Attention takes charge of the complex formation by correlating the whole sentence, and BiLSTM extracts context rep-resentations to tackle the lexical variation of attended embedding in preceding and succeeding directions. Besides, to measure and compare the performance of SA-BiLSTM model, we preprocessed and normalized the Roman Urdu sentences. Due to the efficient design of SA-BiLSTM, it can use fewer computation resources and yield a high accuracy of 68.4% and 69.3% on preprocessed and normalized datasets, respectively, which indicate that SA-BiLSTM can achieve better efficiency as compared with other state-of-the-art deep architectures.

@article{Manzoor2020,
  title     = {Lexical Variation and Sentiment Analysis of Roman Urdu Sentences with Deep Neural Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {2},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Arslan Manzoor and Saqib Mamoon and Song Kei Tao and Ali Zakir and Muhammad Adil and Jianfeng Lu},
  doi       = {10.14569/IJACSA.2020.0110290},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110290}
}