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Article
Publication date: 24 November 2022

Nao Li, Xiaoyu Yang, IpKin Anthony Wong, Rob Law, Jing Yang Xu and Binru Zhang

This paper aims to classify the sentiment of online tourism-hospitality reviews at an aspect level. A new aspect-oriented sentiment classification method is proposed based on a…

358

Abstract

Purpose

This paper aims to classify the sentiment of online tourism-hospitality reviews at an aspect level. A new aspect-oriented sentiment classification method is proposed based on a neural network model.

Design/methodology/approach

This study constructs an aspect-oriented sentiment classification model using an integrated four-layer neural network: the bidirectional encoder representation from transformers (BERT) word vector model, long short-term memory, interactive attention-over-attention (IAOA) mechanism and a linear output layer. The model was trained, tested and validated on an open training data set and 92,905 reviews extrapolated from restaurants in Tokyo.

Findings

The model achieves significantly better performance compared with other neural networks. The findings provide empirical evidence to validate the suitability of this new approach in the tourism-hospitality domain.

Research limitations/implications

More sentiments should be identified to measure more fine-grained tourism-hospitality experience, and new aspects are recommended that can be automatically added into the aspect set to provide dynamic support for new dining experiences.

Originality/value

This study provides an update to the literature with respect to how a neural network could improve the performance of aspect-oriented sentiment classification for tourism-hospitality online reviews.

研究目的

本文旨在从方面级对在线旅游-酒店评论的情感进行分类。提出了一种基于神经网络模型的面向方面的情感分类新方法。

研究设计/方法/途径

本研究使用集成的四层神经网络构建面向方面的情感分类模型:BERT 词向量模型、LSTM、IAOA 机制和线性输出层。该模型在一个开放的训练数据集和从东京餐厅推断的 92,905 条评论上进行了训练、测试和验证。

研究发现

与其他神经网络相比, 该模型实现了显着更好的性能。研究结果提供了经验证据, 以验证这种新方法在旅游酒店领域的适用性。

研究原创性

该研究提供了有关神经网络如何提高旅游酒店在线评论的面向方面的情感分类性能的新文献。

研究研究局限

应该识别更多的情感从而来更加细化衡量旅游酒店体验, 并推荐新的方面/维度可以被自动添加到方面集中, 为新的用餐体验提供动态支持。

Details

Journal of Hospitality and Tourism Technology, vol. 14 no. 1
Type: Research Article
ISSN: 1757-9880

Keywords

Open Access
Article
Publication date: 19 August 2022

Marlon Santiago Viñán-Ludeña and Luis M. de Campos

The main purpose of this paper is to analyze a tourist destination using sentiment analysis techniques with data from Twitter and Instagram to find the most representative…

3043

Abstract

Purpose

The main purpose of this paper is to analyze a tourist destination using sentiment analysis techniques with data from Twitter and Instagram to find the most representative entities (or places) and perceptions (or aspects) of the users.

Design/methodology/approach

The authors used 90,725 Instagram posts and 235,755 Twitter tweets to analyze tourism in Granada (Spain) to identify the important places and perceptions mentioned by travelers on both social media sites. The authors used several approaches for sentiment classification for English and Spanish texts, including deep learning models.

Findings

The best results in a test set were obtained using a bidirectional encoder representations from transformers (BERT) model for Spanish texts and Tweeteval for English texts, and these were subsequently used to analyze the data sets. It was then possible to identify the most important entities and aspects, and this, in turn, provided interesting insights for researchers, practitioners, travelers and tourism managers so that services could be improved and better marketing strategies formulated.

Research limitations/implications

The authors propose a Spanish-Tourism-BERT model for performing sentiment classification together with a process to find places through hashtags and to reveal the important negative aspects of each place.

Practical implications

The study enables managers and practitioners to implement the Spanish-BERT model with our Spanish Tourism data set that the authors released for adoption in applications to find both positive and negative perceptions.

Originality/value

This study presents a novel approach on how to apply sentiment analysis in the tourism domain. First, the way to evaluate the different existing models and tools is presented; second, a model is trained using BERT (deep learning model); third, an approach of how to identify the acceptance of the places of a destination through hashtags is presented and, finally, the evaluation of why the users express positivity (negativity) through the identification of entities and aspects.

研究目的

这项工作的主要目的是使用情感分析技术和来自 Twitter 和 Instagram 的数据来分析旅游目的地, 以便找到最具代表性的实体(或地点)和用户的感知(或方面)。

研究设计/方法/途径

我们使用 90,725 个 Instagram 帖子和 235,755 个 Twitter 推文来分析格拉纳达(西班牙)的旅游业, 以确定旅行者在两个社交媒体网站上提到的重要地点和看法。我们使用了几种方法对英语和西班牙语文本进行情感分类, 包括深度学习模型。

研究发现

测试集中的最佳结果是使用来自Transformers (BERT) 模型的双向编码器表示 (BERT) 用于西班牙语文本和Tweeteval 用于英语文本, 这些结果随后用于分析我们的数据集。然后可以确定最重要的实体和方面, 这反过来又为研究人员、从业人员、旅行者和旅游管理者提供了有趣的见解, 从而可以改进服务并制定更好的营销策略。

研究局限性

我们提出了一个用于执行情感分类的西班牙旅游 BERT 模型, 以及通过主题标签找到地点并揭示每个地点的重要负面方面的过程。

实践意义

该研究使管理人员和从业人员能够使用我们发布的西班牙旅游数据集实施西班牙-BERT 模型, 以便在应用程序中采用该数据集, 以找到正面和负面的看法。

研究原创性

本研究提出了一种如何在旅游领域应用情感分析的新方法。首先, 介绍了评估不同现有模型和工具的方法; 其次, 使用 BERT(深度学习模型)训练模型; 第三, 提出了如何通过标签识别目的地地点的接受度的方法, 最后通过实体和方面的识别来评估用户表达积极性(消极性)的原因。

Details

Journal of Hospitality and Tourism Technology, vol. 13 no. 5
Type: Research Article
ISSN: 1757-9880

Keywords

Article
Publication date: 29 May 2023

Jinxiang Zeng, Shujin Cao, Yijin Chen, Pei Pan and Yafang Cai

This study analyzed the interdisciplinary characteristics of Chinese research studies in library and information science (LIS) measured by knowledge elements extracted through the…

Abstract

Purpose

This study analyzed the interdisciplinary characteristics of Chinese research studies in library and information science (LIS) measured by knowledge elements extracted through the Lexicon-LSTM model.

Design/methodology/approach

Eight research themes were selected for experiment, with a large-scale (N = 11,625) dataset of research papers from the China National Knowledge Infrastructure (CNKI) database constructed. And it is complemented with multiple corpora. Knowledge elements were extracted through a Lexicon-LSTM model. A subject knowledge graph is constructed to support the searching and classification of knowledge elements. An interdisciplinary-weighted average citation index space was constructed for measuring the interdisciplinary characteristics and contributions based on knowledge elements.

Findings

The empirical research shows that the Lexicon-LSTM model has superiority in the accuracy of extracting knowledge elements. In the field of LIS, the interdisciplinary diversity indicator showed an upward trend from 2011 to 2021, while the disciplinary balance and difference indicators showed a downward trend. The knowledge elements of theory and methodology could be used to detect and measure the interdisciplinary characteristics and contributions.

Originality/value

The extraction of knowledge elements facilitates the discovery of semantic information embedded in academic papers. The knowledge elements were proved feasible for measuring the interdisciplinary characteristics and exploring the changes in the time sequence, which helps for overview the state of the arts and future development trend of the interdisciplinary of research theme in LIS.

Details

Aslib Journal of Information Management, vol. 75 no. 3
Type: Research Article
ISSN: 2050-3806

Keywords

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