Extracting topic-sensitive content from textual documents—A hybrid topic model approach

Liang, Yan; Liu, Ying; Chen, Chong; Jiang, Zhigang

doi:10.1016/j.engappai.2017.12.010

Cited by 14 publications

(17 citation statements)

References 22 publications

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“…For example, Wang and Zhai [50] uncovered the two major types of motivation for entering online chat groups, i.e., acquisition of knowledge and sense of belonging, by analysing the chat messages without directly asking the chatters. Likewise, Felix [51], Liang et al [52], and Xiao [53] all successfully recognised users' motivation from their textual expressions.…”

Section: Ugc Analysismentioning

confidence: 95%

Leisure Motivation and Satisfaction: A Text Mining of Yoga Centres, Yoga Consumers, and Their Interactions

Jia

2018

Sustainability

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Understanding the motivation and satisfaction of yoga consumers is of critical importance for both leisure service providers and leisure researchers to enhance the sustainability of personal lives in terms of physical wellness and mental happiness. For this purpose, this study investigated 25,120 pairs of online ratings and reviews from 100 yoga centres in Shanghai, China using latent Dirichlet allocation (LDA)-based text mining, and successfully established the relationship between rating and review. Findings suggest that Chinese yogis are motivated by improving physical condition, improving psychological condition, gracing appearance, establishing social connection, and creating social isolation. In addition to teaching mainstream yoga, yoga centres also provide additional courses. From a consumer perspective, yogis are relatively satisfied with teachers, courses, and the environment, but complain about the supporting staff, membership price, and reservation service. Managerially, yoga centres are encouraged to continue attending to the motivations of yogis, specialising their guidance, and fostering strengths and circumventing weaknesses in their service. This study also contributes by verifying, elaborating on, and tentatively extending the framework of the Physical Activity and Leisure Motivation Scale (PALMS).

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Section: Ugc Analysismentioning

confidence: 95%

Leisure Motivation and Satisfaction: A Text Mining of Yoga Centres, Yoga Consumers, and Their Interactions

Jia

2018

Sustainability

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“…The research by Liang et al [26] found users' motivations by studying users' textual expression on the Internet. In addition, finding correlations between users and the number of ratings can also be a way to quantify these methodologies in order to obtain metrics for the motivation and satisfaction of Internet users who generate content.…”

Section: Ugc Analysismentioning

confidence: 99%

“…In the research by Liang [26], a semi-supervised dual recurrent neural network was used to perform Sentiment Analysis. This is similar to a traditional neural network and can be used to evaluate a data set over a long period of time [6,27].…”

Section: Sentiment Analysis With Social Network Analysismentioning

confidence: 99%

Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

2019

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The main aim of this study is to identify the key factors in User Generated Content (UGC) on the Twitter social network for the creation of successful startups, as well as to identify factors for sustainable startups and business models. New technologies were used in the proposed research methodology to identify the key factors for the success of startup projects. First, a Latent Dirichlet Allocation (LDA) model was used, which is a state-of-the-art thematic modeling tool that works in Python and determines the database topic by analyzing tweets for the #Startups hashtag on Twitter (n = 35.401 tweets). Secondly, a Sentiment Analysis was performed with a Supervised Vector Machine (SVM) algorithm that works with Machine Learning in Python. This was applied to the LDA results to divide the identified startup topics into negative, positive, and neutral sentiments. Thirdly, a Textual Analysis was carried out on the topics in each sentiment with Text Data Mining techniques using Nvivo software. This research has detected that the topics with positive feelings for the identification of key factors for the startup business success are startup tools, technology-based startup, the attitude of the founders, and the startup methodology development. The negative topics are the frameworks and programming languages, type of job offers, and the business angels’ requirements. The identified neutral topics are the development of the business plan, the type of startup project, and the incubator’s and startup’s geolocation. The limitations of the investigation are the number of tweets in the analyzed sample and the limited time horizon. Future lines of research could improve the methodology used to determine key factors for the creation of successful startups and could also study sustainable issues.

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“…Different groups have different characteristics, and individuals with the same attributes may have similarities in tourism behavior [143]. Study [73] has confirmed that differences in tourist attributes can also lead to differences in tourist perceptions. Through the study of the Cape Town tourism market [144], it was found that visitors' age, place of residence, destination stay time, return visits, etc., had an important influence on the perception of tourists, and the sentiments they conveyed [145] also had different characteristics.…”

Section: Tourist Profilementioning

confidence: 76%

“…Besides, as above analysis, LDA ignores the order structure of texts and the meaning of words, so it is one of the research directions that scholars have focused on by exploring the features of words and sentences, etc., to enhance the ability of topic extraction, and will be show great potential in the future. Except for the issue of short text, in business application, users or practitioners may concern different aspects of a topic or aspect related information; the context information of the topic aspect is often used to explore topic-sensitive content [73]. In order to understand a topic more granularly, the structural relation among topics is also a problem, which is widely concerned such as the exploring the hierarchical structure among topics or the global and local relation [74].…”

Section: Topic Extractionmentioning

confidence: 99%

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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Extracting topic-sensitive content from textual documents—A hybrid topic model approach

Cited by 14 publications

References 22 publications

Leisure Motivation and Satisfaction: A Text Mining of Yoga Centres, Yoga Consumers, and Their Interactions

Leisure Motivation and Satisfaction: A Text Mining of Yoga Centres, Yoga Consumers, and Their Interactions

Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

A Review of Text Corpus-Based Tourism Big Data Mining

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