Abstract
Sentiment analysis of online tourist reviews is playing an increasingly important role in tourism. Accurately capturing the attitudes of tourists regarding different aspects of the scenic sites or the overall polarity of their online reviews is key to tourism analysis and application. However, the performances of current document sentiment analysis methods are not satisfactory as they either neglect the topics of the document or do not consider that not all words contribute equally to the meaning of the text. In this work, we propose a bidirectional gated recurrent unit neural network model (BiGRULA) for sentiment analysis by combining a topic model (lda2vec) and an attention mechanism. Lda2vec is used to discover all the main topics of review corpus, which are then used to enrich the word vector representation of words with context. The attention mechanism is used to learn to attribute different weights of the words to the overall meaning of the text. Experiments over 20 NewsGroup and IMDB datasets demonstrate the effectiveness of our model. Furthermore, we applied our model to hotel review data analysis, which allows us to get more coherent topics from these reviews and achieve good performance in sentiment classification.
Highlights
The availability of extensive tourism online reviews provides an unprecedented opportunity to analyze the emotions, preferences, feelings, and opinions expressed by visitors
We present a new approach (BiGRULA) based on a bidirectional gated recurrent unit (BiGRU) neural network for sentiment classification, which combines the topic model, lda2vec, and an attention mechanism
To evaluate the performance of our BiGRULA model for sentiment classification, we first tested how the lda2vec, the topic-enhanced word-vector-encoding method used in our model, performed in document classification compared to other text-encoding methods
Summary
The availability of extensive tourism online reviews provides an unprecedented opportunity to analyze the emotions, preferences, feelings, and opinions expressed by visitors. Serna [4] analyzed the public bike share system in Spain to explore sustainable tourism through sentiment analysis of UGC He [5] used sentiment analysis techniques to analyze online hotel reviews and to understand users’ preferred hotel attributes or demands. Lai [9] replaced traditional window-based neural networks with recurrent structures for text classification These works either neglect the order of the sentences or neglect the global meaning of the document vectors. We present a new approach (BiGRULA) based on a bidirectional gated recurrent unit (BiGRU) neural network for sentiment classification, which combines the topic model, lda2vec, and an attention mechanism. (1) We proposed the BiGRULA recurrent neural network model with topic-enhanced word embedding and an attention mechanism for sentiment classification. (4) We applied our BiGRULA model to a real-world hotel review comment analysis and demonstrated its capability to extract meaningful topics from the reviews and to make accurate sentiment classification
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