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Anis, S., Saad, S. and Aref, M. (2021) Deep Learning-Based Approach for Sentiment Classification of Hotel Reviews. In: Kumar, S., Purohit, S.D., Hiranwal, S., Prasad, M., Eds., Proceedings of International Conference on Communication and Computational Technologies. Algorithms for Intelligent Systems, Springer, Singapore, 211-218.
https://doi.org/10.1007/978-981-16-3246-4_16

has been cited by the following article:

• TITLE: Towards Mining Public Opinion: An Attention-Based Long Short Term Memory Network Using Transfer Learning

  AUTHORS: G. M. Sakhawat Hossain, Md. Harun Or Rashid, Md. Rafiqul Islam, Ananya Sarker, Must. Asma Yasmin

  KEYWORDS: Opinion Mining, Deep Learning, Word2Vec, Attention-LSTM, Transfer Learning

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.6, June 30, 2022

  ABSTRACT: The Internet provides a large number of tools and resources, such as social media sites, online newsgroups, blogs, electronic forums, virtual communities, and online travel sites, for consumers to express their views or opinions regarding various issues. These opinions can help organizations like tourism to improve their products and services for their consumers. Opinion mining refers to a process of identifying emotions by applying Natural Language Processing (NLP) techniques to a pool of texts. This paper mainly focuses on mining public opinion from the hotel reviews domain. To do so, we proposed a novel technique called the Attention-Based Long Short Term Memory (Attention-LSTM) Network using a transfer learning approach. We empirically analyzed several machine learning and deep learning methods and observed our proposed technique provided an adequate performance for mining public opinion in the hotel reviews domain.