Authors:
Agorakis Bompotas
1
;
Aristidis Ilias
1
;
Andreas Kanavos
2
;
Panayiotis Kechagias
1
;
Panayiotis Arvanitakis
3
;
Nikos Zotos
3
;
Konstantinos Kovas
3
and
Christos Makris
1
Affiliations:
1
Computer Engineering and Informatics Department, University of Patras, Patras, Greece
;
2
Department of Digital Media and Communication, Ionian University, Kefalonia, Greece
;
3
Innovative Private Company, Patras, Greece
Keyword(s):
Deep Learning, LSTM Neural Networks, Machine Learning, Natural Language Processing, Sentiment Analysis.
Abstract:
With the advent of social media, there is a data abundance so that analytics can be reliably designed for ultimately providing valuable information towards a given product or service. 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. In this paper, we delve into the detail of design and implementation of a system that initially utilizes some pre-processing 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 main aspects of this system have been described in (Bompotas et al., 2020a) and (Bompotas et al., 2020b). In the present article we essen
tially refactor the system that was described in and by embedding in the implementation the Latent Dirichlet Allocation (LDA) component and perform a repeatibility study on the experimental findings that were reported in (Bompotas et al., 2020a) depicting that its experimental findings are valid.
(More)