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

2020 ◽  
pp. 109467052097514
Author(s):  
Fei Ye ◽  
Qian Xia ◽  
Minhao Zhang ◽  
Yuanzhu Zhan ◽  
Yina Li
Keyword(s):  
Latent Dirichlet Allocation ◽  
Nearest Neighbor ◽  
Service Industry ◽  
Analytical Framework ◽  
Online Reviews ◽  
Service Industries ◽  
K Nearest Neighbor ◽  
Allocation Model ◽  
Customer Reviews ◽  
Latent Dirichlet Allocation Model

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.

scholarly journals Speeding up calibration of latent Dirichlet allocation model to improve topic analysis in software engineering

2021 ◽  
Author(s):  
Jorge Arturo Lopez
Keyword(s):  
Software Engineering ◽  
Simple Formula ◽  
Latent Dirichlet Allocation ◽  
Allocation Model ◽  
Topic Analysis ◽  
Latent Dirichlet Allocation Model ◽  
Related Text ◽  
The Empirical Analysis ◽  
Large Corpus ◽  
Dirichlet Allocation

Extraction of topics from large text corpuses helps improve Software Engineering (SE) processes. Latent Dirichlet Allocation (LDA) represents one of the algorithmic tools to understand, search, exploit, and summarize a large corpus of data (documents), and it is often used to perform such analysis. However, calibration of the models is computationally expensive, especially if iterating over a large number of topics. Our goal is to create a simple formula allowing analysts to estimate the number of topics, so that the top X topics include the desired proportion of documents under study. We derived the formula from the empirical analysis of three SE-related text corpuses. We believe that practitioners can use our formula to expedite LDA analysis. The formula is also of interest to theoreticians, as it suggests that different SE text corpuses have similar underlying properties.

scholarly journals Exploiting Language Models to Classify Events from Twitter

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Duc-Thuan Vo ◽  
Vo Thuan Hai ◽  
Cheol-Young Ock
Keyword(s):  
Latent Dirichlet Allocation ◽  
Nearest Neighbor ◽  
Language Models ◽  
K Nearest Neighbor ◽  
Text Corpora ◽  
Common Term ◽  
Selectional Preferences ◽  
Linguistic Relations ◽  
Relationship Of ◽  
Learning Language

Classifying events is challenging in Twitter because tweets texts have a large amount of temporal data with a lot of noise and various kinds of topics. In this paper, we propose a method to classify events from Twitter. We firstly find the distinguishing terms between tweets in events and measure their similarities with learning language models such as ConceptNet and a latent Dirichlet allocation method for selectional preferences (LDA-SP), which have been widely studied based on large text corpora within computational linguistic relations. The relationship of term words in tweets will be discovered by checking them under each model. We then proposed a method to compute the similarity between tweets based on tweets’ features including common term words and relationships among their distinguishing term words. It will be explicit and convenient for applying to k-nearest neighbor techniques for classification. We carefully applied experiments on the Edinburgh Twitter Corpus to show that our method achieves competitive results for classifying events.

Evaluating Annotated Dataset of Customer Reviews for Aspect Based Sentiment Analysis

2021 ◽  
Author(s):  
Dimple Chehal ◽  
Parul Gupta ◽  
Payal Gulati
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Product Reviews ◽  
K Nearest Neighbor ◽  
Customer Reviews ◽  
Percent Accuracy

Sentiment analysis of product reviews on e-commerce platforms aids in determining the preferences of customers. Aspect-based sentiment analysis (ABSA) assists in identifying the contributing aspects and their corresponding polarity, thereby allowing for a more detailed analysis of the customer’s inclination toward product aspects. This analysis helps in the transition from the traditional rating-based recommendation process to an improved aspect-based process. To automate ABSA, a labelled dataset is required to train a supervised machine learning model. As the availability of such dataset is limited due to the involvement of human efforts, an annotated dataset has been provided here for performing ABSA on customer reviews of mobile phones. The dataset comprising of product reviews of Apple-iPhone11 has been manually annotated with predefined aspect categories and aspect sentiments. The dataset’s accuracy has been validated using state-of-the-art machine learning techniques such as Naïve Bayes, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbor and Multi Layer Perceptron, a sequential model built with Keras API. The MLP model built through Keras Sequential API for classifying review text into aspect categories produced the most accurate result with 67.45 percent accuracy. K- nearest neighbor performed the worst with only 49.92 percent accuracy. The Support Vector Machine had the highest accuracy for classifying review text into aspect sentiments with an accuracy of 79.46 percent. The model built with Keras API had the lowest 76.30 percent accuracy. The contribution is beneficial as a benchmark dataset for ABSA of mobile phone reviews.

scholarly journals Crowd Event Perception Based on Spatiotemporal Weber Field

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Zhou Su ◽  
Hua Wei ◽  
Sha Wei
Keyword(s):  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Interaction Force ◽  
Motion Modeling ◽  
Allocation Model ◽  
Motion Patterns ◽  
Crowd Management ◽  
Latent Dirichlet Allocation Model ◽  
Spatiotemporal Signal ◽  
Crowd Motion

Over the past decade, a wide attention has been paid to the crowd control and management in intelligent video surveillance area. Among the tasks of automatic video-based crowd management, crowd motion modeling is recognized as one of the most critical components, since it lays a crucial foundation for numerous subsequent analyses. However, it still encounters many unsolved challenges due to occlusions among pedestrians, complicated motion patterns in crowded scenarios, and so forth. Addressing these issues, we propose a novel spatiotemporal Weber field, which integrates both appearance characteristics and stimulus of crowd motion patterns, to recognize the large-scale crowd event. On the one hand, crowd motion is recognized as variations of spatiotemporal signal, and we then measure the variation based on Weber law. The result is referred to as spatiotemporal Weber variation feature. On the other hand, motivated by the achievements in crowd dynamics that crowd motion has a close relationship with interaction force, we propose a spatiotemporal Weber force feature to exploit the stimulus of crowd behaviors. Finally, we utilize the latent Dirichlet allocation model to establish the relationship between crowd events and crowd motion patterns. Experiments on PETS2009 and UMN databases demonstrate that our proposed method outperforms the previous methods for the large-scale crowd behavior perception.

Engineering for Global Development: Characterizing the Discipline Through a Systematic Literature Review

2020 ◽  
Author(s):  
Grace Burleson ◽  
Jesse Austin-Breneman
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Mechanical Engineering ◽  
Latent Dirichlet Allocation ◽  
Descriptive Analysis ◽  
Secondary Data ◽  
Research Area ◽  
Global Development ◽  
Allocation Model ◽  
Latent Dirichlet Allocation Model

Abstract Over the past 50 years, researchers have repeatedly proposed the establishment of a new interdisciplinary engineering field in Engineering for Global Development (EGD), whose analytical tools and design processes result in positive social impacts and poverty alleviation in a global development context. Within each discipline and research area, a growing body of work has sought to systematically create scientific knowledge in this area. However, a recent network analysis of Human-Centered Design plus Development research indicates that sub-communities are not collaborating at a high level and therefore the overall research agenda may lack cohesion. This paper presents a descriptive analysis of EGD research within mechanical engineering along four dimensions through a systematic literature review and secondary data analysis. Results from the review and a Latent Dirichlet Allocation model indicate EGD work in mechanical engineering draws upon research methodologies from a number of other fields and has low levels of consensus on technical terminology. These results suggest consensus in the broader interdisciplinary EGD field should be examined.

scholarly journals Inferring Concept Prerequisite Relations from Online Educational Resources

2019 ◽  
Vol 33 ◽  
pp. 9589-9594 ◽  
Author(s):  
Sudeshna Roy ◽  
Meghana Madhyastha ◽  
Sheril Lawrence ◽  
Vaibhav Rajan
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Training Data ◽  
Allocation Model ◽  
Latent Dirichlet Allocation Model ◽  
Technology Applications ◽  
Benchmark Datasets ◽  
Latent Representations ◽  
Online Educational Resources

The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum creation. We present PREREQ, a new supervised learning method for inferring concept prerequisite relations. PREREQ is designed using latent representations of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a neural network based on the Siamese network architecture. PREREQ can learn unknown concept prerequisites from course prerequisites and labeled concept prerequisite data. It outperforms state-of-the-art approaches on benchmark datasets and can effectively learn from very less training data. PREREQ can also use unlabeled video playlists, a steadily growing source of training data, to learn concept prerequisites, thus obviating the need for manual annotation of course prerequisites.

scholarly journals An Influence Prediction Model for Microblog Entries on Public Health Emergencies

2019 ◽  
Vol 3 (2) ◽  
pp. 102-115 ◽  
Author(s):  
Lu An ◽  
Xingyue Yi ◽  
Yuxin Han ◽  
Gang Li
Keyword(s):  
Public Health ◽  
Prediction Model ◽  
Latent Dirichlet Allocation ◽  
Prediction Models ◽  
Data Sets ◽  
Allocation Model ◽  
Latent Dirichlet Allocation Model ◽  
Public Health Emergencies ◽  
Proposed Model ◽  
The Individual

Abstract This study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.

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