Clustering helps to improve price prediction in online booking systems

2021 ◽  
Vol 17 (1) ◽  
pp. 45-53
Author(s):  
Le Hong Trang ◽  
Tran Duong Huy ◽  
Anh Ngoc Le
Keyword(s):  
Machine Learning ◽  
Empirical Study ◽  
Sentiment Analysis ◽  
Design Methodology ◽  
Prediction Performance ◽  
Experimental Results ◽  
Data Sets ◽  
Classification Models ◽  
Content Type ◽  
Price Prediction

Purpose Pricing on the online booking systems is a difficult task for the host, the systems usually set the prices that are lower than the general premises and quality, and that only gives benefits to the system by easily attracting the customer to use the service. The setting price of the new accommodation is often based on location, the number of beds, type of house and so on. The main problem is to predict the most reasonable price for the host. This paper aims to study the use of machine learning and sentiment analysis for predicting the price of online booking systems. Design/methodology/approach In particular, an empirical study is performed first for some well-known classification models for the problems. The authors then propose to apply k-means, a clustering technique, together with Gradient Boost and XGBoost models to improve the prediction performance. Experiments are conducted and tested for real Airbnb data sets collected in London City. Findings Experimental results are given and compared to show that the authors’ method outperforms to an updated method. Originality/value The authors use k-means and sampling together with Gradient Boost and XGBoost models to improve the prediction performance.

scholarly journals Gender bias in machine learning for sentiment analysis

2018 ◽  
Vol 42 (3) ◽  
pp. 343-354 ◽  
Author(s):  
Mike Thelwall
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Gender Bias ◽  
Training Data ◽  
Data Sets ◽  
Content Type ◽  
Female Authors ◽  
Gender Biases ◽  
Mixed Gender ◽  
Gender Specific

Purpose The purpose of this paper is to investigate whether machine learning induces gender biases in the sense of results that are more accurate for male authors or for female authors. It also investigates whether training separate male and female variants could improve the accuracy of machine learning for sentiment analysis. Design/methodology/approach This paper uses ratings-balanced sets of reviews of restaurants and hotels (3 sets) to train algorithms with and without gender selection. Findings Accuracy is higher on female-authored reviews than on male-authored reviews for all data sets, so applications of sentiment analysis using mixed gender data sets will over represent the opinions of women. Training on same gender data improves performance less than having additional data from both genders. Practical implications End users of sentiment analysis should be aware that its small gender biases can affect the conclusions drawn from it and apply correction factors when necessary. Users of systems that incorporate sentiment analysis should be aware that performance will vary by author gender. Developers do not need to create gender-specific algorithms unless they have more training data than their system can cope with. Originality/value This is the first demonstration of gender bias in machine learning sentiment analysis.

scholarly journals Gender bias in sentiment analysis

2018 ◽  
Vol 42 (1) ◽  
pp. 45-57 ◽  
Author(s):  
Mike Thelwall
Keyword(s):  
Sentiment Analysis ◽  
Gender Bias ◽  
Design Methodology ◽  
Data Sets ◽  
Men And Women ◽  
Content Type ◽  
Males And Females ◽  
The Uk ◽  
Gender Specific ◽  
Practical Implications

Purpose The purpose of this paper is to test if there are biases in lexical sentiment analysis accuracy between reviews authored by males and females. Design/methodology/approach This paper uses data sets of TripAdvisor reviews of hotels and restaurants in the UK written by UK residents to contrast the accuracy of lexical sentiment analysis for males and females. Findings Male sentiment is harder to detect because it is less explicit. There was no evidence that this problem could be solved by gender-specific lexical sentiment analysis. Research limitations/implications Only one lexical sentiment analysis algorithm was used. Practical implications Care should be taken when drawing conclusions about gender differences from automatic sentiment analysis results. When comparing opinions for product aspects that appeal differently to men and women, female sentiments are likely to be overrepresented, biasing the results. Originality/value This is the first evidence that lexical sentiment analysis is less able to detect the opinions of one gender than another.

Topic features for machine learning-based sentiment analysis in Indonesian tweets

2019 ◽  
Vol 12 (1) ◽  
pp. 70-81 ◽  
Author(s):  
Hendri Murfi ◽  
Furida Lusi Siagian ◽  
Yudi Satria
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Analysis Data ◽  
Data Representation ◽  
Support Vector ◽  
Data Sets ◽  
Content Type ◽  
Textual Data ◽  
Standard Word

Purpose The purpose of this paper is to analyze topics as alternative features for sentiment analysis in Indonesian tweets. Design/methodology/approach Given Indonesian tweets, the processes of sentiment analysis start by extracting features from the tweets. The features are words or topics. The authors use non-negative matrix factorization to extract the topics and apply a support vector machine to classify the tweets into its sentiment class. Findings The authors analyze the accuracy using the two-class and three-class sentiment analysis data sets. Both data sets are about sentiments of candidates for Indonesian presidential election. The experiments show that the standard word features give better accuracies than the topics features for the two-class sentiment analysis. Moreover, the topic features can slightly improve the accuracy of the standard word features. The topic features can also improve the accuracy of the standard word features for the three-class sentiment analysis. Originality/value The standard textual data representation for sentiment analysis using machine learning is bag of word and its extensions mainly created by natural language processing. This paper applies topics as novel features for the machine learning-based sentiment analysis in Indonesian tweets.

Ontology assessment based on linked data principles

2018 ◽  
Vol 14 (4) ◽  
pp. 453-479
Author(s):  
Leila Zemmouchi-Ghomari ◽  
Kaouther Mezaache ◽  
Mounia Oumessad
Keyword(s):  
Linked Data ◽  
Design Methodology ◽  
State Of The Art ◽  
Experimental Results ◽  
Data Sets ◽  
Content Type ◽  
Detailed Interpretation

Purpose The purpose of this paper is to evaluate ontologies with respect to the linked data principles. This paper presents a concrete interpretation of the four linked data principles applied to ontologies, along with an implementation that automatically detects violations of these principles and fixes them (semi-automatically). The implementation is applied to a number of state-of-the-art ontologies. Design/methodology/approach Based on a precise and detailed interpretation of the linked data principles in the context of ontologies (to become as reusable as possible), the authors propose a set of algorithms to assess ontologies according to the four linked data principles along with means to implement them using a Java/Jena framework. All ontology elements are extracted and examined taking into account particular cases, such as blank nodes and literals. The authors also provide propositions to fix some of the detected anomalies. Findings The experimental results are consistent with the proven quality of popular ontologies of the linked data cloud because these ontologies obtained good scores from the linked data validator tool. Originality/value The proposed approach and its implementation takes into account the assessment of the four linked data principles and propose means to correct the detected anomalies in the assessed data sets, whereas most LD validator tools focus on the evaluation of principle 2 (URI dereferenceability) and principle 3 (RDF validation); additionally, they do not tackle the issue of fixing detected errors.

Learning attitudinal decision model through pair-wise preferences

Kybernetes ◽  
2018 ◽  
Vol 47 (8) ◽  
pp. 1569-1584
Author(s):  
Manish Aggarwal
Keyword(s):  
Machine Learning ◽  
Design Methodology ◽  
Decision Model ◽  
Choice Model ◽  
Weight Vector ◽  
Preference Learning ◽  
Learning Approach ◽  
Data Sets ◽  
Content Type ◽  
Benchmark Data

Purpose This paper aims to learn a decision-maker’s (DM’s) decision model that is characterized in terms of the attitudinal character and the attributes weight vector, both of which are specific to the DM. The authors take the learning information in the form of the exemplary preferences, given by a DM. The learning approach is formalized by bringing together the recent research in the choice models and machine learning. The study is validated on a set of 12 benchmark data sets. Design/methodology/approach The study includes emerging preference learning algorithms. Findings Learning of a DM’s attitudinal choice model. Originality/value Preferences-based learning of a DM’s attitudinal decision model.

Hybrid features prediction model of movie quality using Multi-machine learning techniques for effective business resource planning

2021 ◽  
Vol 40 (5) ◽  
pp. 9361-9382 ◽  
Author(s):  
Naeem Iqbal ◽  
Rashid Ahmad ◽  
Faisal Jamil ◽  
Do-Hyeun Kim
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Resource Planning ◽  
Experimental Results ◽  
Quality Prediction ◽  
Classification Models ◽  
Hybrid Features ◽  
Social Media Data ◽  
Media Data

Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc.

“Please tidy up before leaving”: nudging Airbnb guests toward altruistic behavior

2019 ◽  
Vol 13 (4) ◽  
pp. 524-530 ◽  
Author(s):  
Paul Ranson ◽  
Daniel Guttentag
Keyword(s):  
Social Presence ◽  
Design Methodology ◽  
Field Experiments ◽  
Experimental Results ◽  
Altruistic Behavior ◽  
Content Type ◽  
The Social ◽  
Relationship Model ◽  
Social Market ◽  
Practical Implications

Purpose This study aimed to investigate whether increasing the social presence within an Airbnb lodging environment could nudge guests toward altruistic cleaning behaviors. Design/methodology/approach The study was based around a theoretical framework combining the social-market versus money-market relationship model, nudge theory and social presence theory. A series of three field experiments were conducted, in which social presence was manipulated to test its impact on guest cleaning behaviors prior to departure. Findings The experimental results confirmed the underlying hypothesis that an Airbnb listing’s enhanced social presence can subtly induce guests to help clean their rental units prior to departure. Originality/value This study is the first to examine behavioral nudging in an Airbnb context. It is also one of the first field experiments involving Airbnb. The study findings offer clear theoretical and practical implications.

Feasibility study of automatically performing the concrete delivery dispatching through machine learning techniques

2015 ◽  
Vol 22 (5) ◽  
pp. 573-590 ◽  
Author(s):  
Mojtaba Maghrebi ◽  
Claude Sammut ◽  
S. Travis Waller
Keyword(s):  
Machine Learning ◽  
Human Resources ◽  
Design Methodology ◽  
Machine Learning Techniques ◽  
Mixing Process ◽  
Content Type ◽  
Practical Solution ◽  
Learning Techniques ◽  
Ready Mixed Concrete ◽  
Practical Implications

Purpose – The purpose of this paper is to study the implementation of machine learning (ML) techniques in order to automatically measure the feasibility of performing ready mixed concrete (RMC) dispatching jobs. Design/methodology/approach – Six ML techniques were selected and tested on data that was extracted from a developed simulation model and answered by a human expert. Findings – The results show that the performance of most of selected algorithms were the same and achieved an accuracy of around 80 per cent in terms of accuracy for the examined cases. Practical implications – This approach can be applied in practice to match experts’ decisions. Originality/value – In this paper the feasibility of handling complex concrete delivery problems by ML techniques is studied. Currently, most of the concrete mixing process is done by machines. However, RMC dispatching still relies on human resources to complete many tasks. In this paper the authors are addressing to reconstruct experts’ decisions as only practical solution.

Improving recommender systems’ performance on cold-start users and controversial items by a new similarity model

2016 ◽  
Vol 12 (2) ◽  
pp. 126-149 ◽  
Author(s):  
Masoud Mansoury ◽  
Mehdi Shajari
Keyword(s):  
Real World ◽  
Design Methodology ◽  
Cold Start ◽  
Selection Function ◽  
Data Sets ◽  
Real World Data ◽  
Content Type ◽  
User Similarity ◽  
Active User ◽  
Similarity Model

Purpose This paper aims to improve the recommendations performance for cold-start users and controversial items. Collaborative filtering (CF) generates recommendations on the basis of similarity between users. It uses the opinions of similar users to generate the recommendation for an active user. As a similarity model or a neighbor selection function is the key element for effectiveness of CF, many variations of CF are proposed. However, these methods are not very effective, especially for users who provide few ratings (i.e. cold-start users). Design/methodology/approach A new user similarity model is proposed that focuses on improving recommendations performance for cold-start users and controversial items. To show the validity of the authors’ similarity model, they conducted some experiments and showed the effectiveness of this model in calculating similarity values between users even when only few ratings are available. In addition, the authors applied their user similarity model to a recommender system and analyzed its results. Findings Experiments on two real-world data sets are implemented and compared with some other CF techniques. The results show that the authors’ approach outperforms previous CF techniques in coverage metric while preserves accuracy for cold-start users and controversial items. Originality/value In the proposed approach, the conditions in which CF is unable to generate accurate recommendations are addressed. These conditions affect CF performance adversely, especially in the cold-start users’ condition. The authors show that their similarity model overcomes CF weaknesses effectively and improve its performance even in the cold users’ condition.

Stairways to heaven: implementing social media in organizations

2013 ◽  
Vol 17 (5) ◽  
pp. 741-754 ◽  
Author(s):  
Moria Levy
Keyword(s):  
Social Media ◽  
Knowledge Management ◽  
Empirical Study ◽  
Design Methodology ◽  
Applied Research ◽  
Service Marketing ◽  
Content Type ◽  
Four Levels ◽  
Functional Components ◽  
Practical Implications

Purpose – This paper is aimed at both researchers and organizations. For researchers, it seeks to provide a means for better analyzing the phenomenon of social media implementation in organizations as a knowledge management (KM) enabler. For organizations, it seeks to suggest a step-by-step architecture for practically implementing social media and benefiting from it in terms of KM. Design/methodology/approach – The research is an empirical study. A hypothesis was set; empirical evidence was collected (from 34 organizations). The data were analyzed both quantitatively and qualitatively, thereby forming the basis for the proposed architecture. Findings – Implementing social media in organizations is more than a yes/no question; findings show various levels of implementation in organizations: some implementing at all levels, while others implement only tools, functional components, or even only visibility. Research limitations/implications – Two main themes should be further tested: whether the suggested architecture actually yields faster/eased KM implementation compared to other techniques; and whether it can serve needs beyond the original scope (KM, Israel) as tested in this study (i.e. also for other regions and other needs – service, marketing and sales, etc.). Practical implications – Organizations can use the suggested four levels architecture as a guideline for implementing social media as part of their KM efforts. Originality/value – This paper is original and innovative. Previous studies describe the implementation of social media in terms of yes/no; this research explores the issue as a graded one, where organizations can and do implement social media step-by-step. The paper's value is twofold: it can serve as a foundational study for future researches, which can base their analysis on the suggested architecture of four levels of implementation. It also serves as applied research that will help organizations searching for social media implementation KM enablers.

Sign in / Sign up

Export Citation Format

Share Document