Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach

Ren, Gang; Hong, Taeho

doi:10.3390/su9101765

Cited by 51 publications

(34 citation statements)

References 53 publications

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“…They usually contain multiple topics but cannot be attributed to one certain aspect or target. Therefore, text classification in the current tourism field is usually carried out by topic extraction, to extract all aspects of the text for targeted analysis [22,75].…”

Section: Text Classificationmentioning

confidence: 99%

“…This method is simple, but the accuracy of the result is low due to the existence of the virtual target and the implicit evaluation object [109]. The study [22] extracted topics from the destination reviews based on LDA and then analyzed the sentiment state of each topic in more detail or for finer gain. The study [110] used text mining and sentiment analysis techniques to analyze hotel online reviews to explore the characteristics of hotel products that visitors were more concerned about.…”

Section: Sentiment Analysismentioning

confidence: 99%

“…How to recognize and respond to visitors' behaviors and needs quickly and identify potential customers have become essential factors for the success of tourism stakeholders. By exploiting the subjective information contained in tourism text data, we can assist tourism stakeholders to provide better services for tourists.A large number of text mining techniques have been proposed and applied to tourism text data analysis for creating tourist profiles [8][9][10][11][12][13][14][15] and making effective market supervision [16][17][18][19][20][21][22][23][24][25]. These approaches exploit a variety of text representation strategies [26][27][28][29][30][31][32] and use different NLP techniques for topic extraction [33], text classification [34], sentiment analysis [35], and text clustering [36].…”

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confidence: 99%

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A Review of Text Corpus-Based Tourism Big Data Mining

Zhang

et al. 2019

Applied Sciences

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With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists' opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years. Appl. Sci. 2019, 9, 3300 2 of 27 the travel. In order to utilize this user-generated content properly and further to meet the needs of tourists and promote the tourism industry, we need to analyze and exploit tourists' needs and opinions, and then identify the problems of tourism services or destinations, which has become a new path for tourism development. Besides, as tourism needs become increasingly personalized, visitors begin to pursue self-likeness, self-worth, and diversified travel experiences, and they are no longer willing to endure delays or waits. How to recognize and respond to visitors' behaviors and needs quickly and identify potential customers have become essential factors for the success of tourism stakeholders. By exploiting the subjective information contained in tourism text data, we can assist tourism stakeholders to provide better services for tourists.A large number of text mining techniques have been proposed and applied to tourism text data analysis for creating tourist profiles [8][9][10][11][12][13][14][15] and making effective market supervision [16][17][18][19][20][21][22][23][24][25]. These approaches exploit a variety of text representation strategies [26][27][28][29][30][31][32] and use different NLP techniques for topic extraction [33], text classification [34], sentiment analysis [35], and text clustering [36]. Moreover, while aiming to make computers understand human language, NLP has become the essential tool for text data analysis and is undergoing fast-pace growing based on the applications of deep learning in word embedding, syntax analysis, machine translation, and text understanding. Machine learning-based NLP techniques have been widely used in tourism text analysis, with superior results [19,25]. In addition, due to its high capability for extracting selective and invariant features from texts, and its independency of prior knowledge and linguistic resources, deep learning has been reported to achieve higher performance than other approaches on many NL...

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Section: Text Classificationmentioning

confidence: 99%

Section: Sentiment Analysismentioning

confidence: 99%

mentioning

confidence: 99%

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A Review of Text Corpus-Based Tourism Big Data Mining

Zhang

et al. 2019

Applied Sciences

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“…There exist some studies in information diffusion based on sentiment [14][15][16], but they do not consider the opinion flow between communities. Other researchers analyzed how to visualize topics and opinions in SNS [17][18][19][20][21], but they lack in the information process flow. In this research, a new semantic hidden Markov model (HMM) for discovering information diffusion, named SentiFlow, is introduced to discover probabilistic information flow in consideration of topics and sentiment.…”

Section: Introductionmentioning

confidence: 99%

SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks

Carrera

Jung

2018

Sustainability

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Abstract:In this digital era, people can become more interconnected as information spreads easily and quickly through online social media. The rapid growth of the social network services (SNS) increases the need for better methodologies for comprehending the semantics among the SNS users. This need motivated the proposal of a novel framework for understanding information diffusion process and the semantics of user comments, called SentiFlow. In this paper, we present a probabilistic approach to discover an information diffusion process based on an extended hidden Markov model (HMM) by analyzing the users and comments from posts on social media. A probabilistic dissemination of information among user communities is reflected after discovering topics and sentiments from the user comments. Specifically, the proposed method makes the groups of users based on their interaction on social networks using Louvain modularity from SNS logs. User comments are then analyzed to find different sentiments toward a subject such as news in social networks. Moreover, the proposed method is based on the latent Dirichlet allocation for topic discovery and the naïve Bayes classifier for sentiment analysis. Finally, an example using Facebook data demonstrates the practical value of SentiFlow in real world applications.

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“…In order to enhance the customer satisfaction associated with the shopping experience, almost every e-commerce transection platform has designed the function of user evaluation. These reviews are termed as quite critical for the consumers, businesses, and manufacturers [1], since they not only impact the consumers' shopping decisions [2,3] or word-of-mouth intention [4] and merchants' purchasing strategies but also impact the design and improvement of the products [1,5]. Therefore, consumer reviews are a substantial information resource [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

2018

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How to acquire useful information intelligently in the age of information explosion has become an important issue. In this context, sentiment analysis emerges with the growth of the need of information extraction. One of the most important tasks of sentiment analysis is feature extraction of entities in consumer reviews. This paper first constitutes a directed bipartite feature-sentiment relation network with a set of candidate features-sentiment pairs that is extracted by dependency syntax analysis from consumer reviews. Then, a novel method called MHITS which combines PMI with weighted HITS algorithm is proposed to rank these candidate product features to find out real product features. Empirical experiments indicate the effectiveness of our approach across different kinds and various data sizes of product. In addition, the effect of the proposed algorithm is not the same for the corpus with different proportions of the word pair that includes the "bad", "good", "poor", "pretty good", "not bad" these general collocation words.

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Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach

Cited by 51 publications

References 53 publications

A Review of Text Corpus-Based Tourism Big Data Mining

A Review of Text Corpus-Based Tourism Big Data Mining

SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks

An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

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