A Sentiment-based Hotel Review Summarization using LSTM Neural Networks
Agorakis Bompotas, Aristidis Ilias, Maria Adamopoulos, Andreas Kanavos, Christos H. Makris, Gerasimos Rompolas, Alkiviadis Savvopoulos · 2020
Hotel customers express reviews for every accommodation service provided and/or for the accommodation as a whole. On the other hand, reviews are particularly interested for the tourism industry in order to extract customers' opinions and aspects, which will assist them to improve their provided services. The reviews can be thoroughly found in the Web and their quantity can be considered as big data in Natural Language Processing. In this paper, we have designed an architecture and have implemented a system that initially utilizes some preprocessing techniques, as classic Natural Language Processing approaches, namely TF-IDF bag of words and word embeddings, are employed. These approaches can be further used as the input of various classifiers and Long Short Term Memory Neural Networks. The accuracy of these classifiers is evaluated on a dataset consisting of several reviews for the well known evaluation metrics, namely Precision, Recall and Fl-Measure.