Topic analysis of online reviews for two competitive products using latent Dirichlet allocation

Wang, Wenxin; Feng, Yi; Dai, Wenqiang

doi:10.1016/j.elerap.2018.04.003

Cited by 126 publications

(55 citation statements)

References 38 publications

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“…After removing the other forms of words, only nouns and adjectives are left. We also include bigram [ 50 ] words in the data to extract more valuable information. A bigram is a set of two adjacent words: for example, ‘machine’ and ‘learning’ could be combined into the bigram ‘machine_learning’.…”

Section: Methodsmentioning

confidence: 99%

“…LDA has several advantages for literature analysis. First, LDA is highly efficient for dealing with big data as it can capture effectively text-specific dimensions and does not make any assumption [ 50 ]. Second, LDA incorporates several steps of text analysis with little human intervention, for example, data sampling, and thus the result of topic modelling is more realistic and objective.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An overview of literature on COVID-19, MERS and SARS: Using text mining and latent Dirichlet allocation

Cheng¹,

Cao²,

Liao³

2020

Journal of Information Science

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The unprecedented outbreak of COVID-19 is one of the most serious global threats to public health in this century. During this crisis, specialists in information science could play key roles to support the efforts of scientists in the health and medical community for combatting COVID-19. In this article, we demonstrate that information specialists can support health and medical community by applying text mining technique with latent Dirichlet allocation procedure to perform an overview of a mass of coronavirus literature. This overview presents the generic research themes of the coronavirus diseases: COVID-19, MERS and SARS, reveals the representative literature per main research theme and displays a network visualisation to explore the overlapping, similarity and difference among these themes. The overview can help the health and medical communities to extract useful information and interrelationships from coronavirus-related studies.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An overview of literature on COVID-19, MERS and SARS: Using text mining and latent Dirichlet allocation

Cheng¹,

Cao²,

Liao³

2020

Journal of Information Science

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“…According to Tan et al (2018), although online reviews cannot directly lead to value generation for companies, they can result in internal process development and actionable information for decision making if proper analytical approaches are in place. For example, an analytical approach can be developed to enable managers to collect all their online reviews and other sources of information on their interactions with consumers, so that they can evaluate the competitive environment effectively and respond to consumers' feedback in a timely manner (W. Wang, Yi, and Dai 2018). Also, the company's weaknesses and strengths can be evaluated, which in turn will allow managers to develop appropriate marketing strategies and service improvements.…”

Section: Online Reviews For Value Co-creation and Service Improvementmentioning

confidence: 99%

Harvesting Online Reviews to Identify the Competitor Set in a Service Business: Evidence From the Hotel Industry

Qian

Zhang

et al. 2020

Journal of Service Research

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In today’s global service industry, online reviews posted by consumers offer critical information that influences subsequent consumers’ purchasing decisions and firms’ operation strategies. However, little research has been done on how the same information can be used to identify key competitors and improve services to increase competitiveness. In this article, we propose an analytical framework based on an improved k-nearest neighbor model and a latent Dirichlet allocation model for service managers to harvest online reviews to identify their key competitors and to evaluate the strengths and weaknesses of their businesses. With a sample comprising over 8 million customer reviews of 6,409 hotels in 50 Chinese cities from Ctrip.com , we validate the effectiveness of the proposed approach in the analysis of a hotel’s service competitiveness and its key competitors. The findings indicate that the importance of particular attributes of a hotel varies in different segments according to hotel star ratings. This study extends the literature by bridging online reviews and competitor identification for service industries. It also contributes to practice by offering a systematic and effective way for managers to identify their key competitors, monitor market preferences, ensure service quality, and formulate effective marketing strategies.

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“…Özdağoğlu et al [37] integrated quality function deployment and topic modeling to analyze different topics related to the VoC data. Wang et al [38] investigated unique customer preferences of two competitive products using an LDA-based topic modeling technique. Due to the success of the LDA in topic modeling, we propose to apply LDA to understand different topics of customer needs within a product ecosystem.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems

Zhou

Ayoub

et al. 2019

Journal of Mechanical Design

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Creating product ecosystems has been one of the strategic ways to enhance user experience and business advantages. Among many, customer needs analysis for product ecosystems is one of the most challenging tasks in creating a successful product ecosystem from both the perspectives of marketing research and product development. In this paper, we propose a machine-learning approach to customer needs analysis for product ecosystems by examining a large amount of online user-generated product reviews within a product ecosystem. First, we filtered out uninformative reviews from the informative reviews using a fastText technique. Then, we extract a variety of topics with regard to customer needs using a topic modeling technique named latent Dirichlet allocation. In addition, we applied a rule-based sentiment analysis method to predict not only the sentiment of the reviews but also their sentiment intensity values. Finally, we categorized customer needs related to different topics extracted using an analytic Kano model based on the dissatisfaction-satisfaction pair from the sentiment analysis. A case example of the Amazon product ecosystem was used to illustrate the potential and feasibility of the proposed method.

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Topic analysis of online reviews for two competitive products using latent Dirichlet allocation

Cited by 126 publications

References 38 publications

An overview of literature on COVID-19, MERS and SARS: Using text mining and latent Dirichlet allocation

An overview of literature on COVID-19, MERS and SARS: Using text mining and latent Dirichlet allocation

Harvesting Online Reviews to Identify the Competitor Set in a Service Business: Evidence From the Hotel Industry

A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems

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