This document summarizes research on predicting Yelp ratings for restaurants from user reviews. The author collected review and business data from Yelp and divided it into development, cross-validation, and test sets. Various classifiers including Naive Bayes, SVM, and logistic regression were tested on the cross-validation data, with logistic regression performing best. Feature engineering using POS tags and selecting the top 500 features improved results. Tuning logistic regression with L1 regularization further optimized performance. The author concludes POS features and selecting an optimal number of top features improves predictive accuracy and discusses ideas for future work.
This document summarizes a study that developed a model to predict the number of "useful" votes a Yelp review will receive. It finds that an extreme gradient boosting model (xgbtree2) using only 5 key features can accurately predict review usefulness, with a root-mean-squared error of 0.41-0.49 depending on the business category. The study analyzes Yelp data on over 500,000 restaurant reviews to validate the model, finding it tends to slightly underestimate reviews receiving larger numbers of useful votes. To improve predictions, the model is trained and applied separately to each of 10 major business categories.
Presentation for Information Retrieval / Extraction Project on Yelp Data set. The project utilizes various Information Retrieval and Natural Language Processing concepts to build the models.
This document discusses conjoint analysis and cluster analysis, which are multivariate statistical techniques. Conjoint analysis is used to understand how consumers develop preferences for products/services based on attribute combinations. Cluster analysis groups observations into similar clusters to segment markets for targeting customers. The document provides examples of each technique applied to developing an industrial cleanser and measuring customer loyalty. It explains key concepts for each technique like attributes, profiles, distances, linkages, and determining optimal cluster numbers.
User service-rating-prediction-by-exploring social users rating BehavioursShakas Technologies
This document proposes a user-service rating prediction approach that explores social users' rating behaviors. It focuses on aspects of users' rating behaviors like when they rate items, what the ratings and items are, and how ratings diffuse among social friends. The approach represents rating schedules and models interpersonal rating diffusion. It fuses personal interest factors, interpersonal interest similarity, rating behavior similarity, and rating diffusion into a matrix factorization framework. The proposed approach aims to address limitations of existing collaborative filtering recommender systems like increased computational costs, lack of privacy, and insecure computations.
Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Lea...jianjinshu
This document discusses a trust-based rating prediction approach for recommending content in collaborative learning social software. It presents a 3A interaction model to build users' trust networks based on activities, artifacts, and actors. The approach measures direct and indirect trust to infer trust values implicitly. It then predicts ratings for items by taking the weighted average of ratings from trustable users in one's network. The approach is evaluated on a dataset and shown to improve over simple averaging. Future work involves deploying and evaluating the approach on a collaborative learning platform.
This document describes research conducted to predict star ratings for Yelp reviews using language feature analysis. The researchers used linear regression models to analyze basic readily available features from the Yelp dataset, including business star ratings, user average ratings, and review vote counts. Topic models using latent Dirichlet allocation (LDA) were also analyzed as advanced language features extracted from review texts. Stemming text prior to LDA improved predictive performance compared to the baseline model. The best performing model used a combination of basic features and LDA topic distributions, reducing mean squared error over the baseline.
A Supervised Modeling Approach to Determine Elite Status of Yelp MembersJennifer (Hui) Li
The document describes research conducted by a team of students at Carnegie Mellon University to predict whether a user on the Yelp platform will obtain elite status. The team analyzed attributes of users in the Yelp Academic Dataset to identify correlations with elite status. They found that number of reviews, number of fans, and counts of useful, cool, and funny votes showed significant differences between elite and non-elite users. The team then experimented with various machine learning algorithms like decision trees, k-nearest neighbors, and linear regression to classify users and predict elite status. Their best model achieved a prediction accuracy of 94.2% using a decision tree algorithm.
IRJET- Fake Review Detection using Opinion MiningIRJET Journal
This document summarizes a research paper that aims to develop a method for detecting fake reviews on e-commerce websites. The proposed method uses sentiment analysis and opinion mining techniques to classify reviews as "suspicious", "clear", or "hazy". It first runs reviews through the VADER sentiment analysis tool to assign polarity scores, then calculates vector values based on review length, trigram frequency, and sentiment intensity. Reviews are initially classified using a logic table, with "hazy" reviews undergoing further processing. The results include annotated reviews showing sentiment scores and credibility scores to help users identify trustworthy reviews. Future work could improve the dictionary and sentiment weights to increase accuracy of the classification model.
This document summarizes a study that developed a model to predict the number of "useful" votes a Yelp review will receive. It finds that an extreme gradient boosting model (xgbtree2) using only 5 key features can accurately predict review usefulness, with a root-mean-squared error of 0.41-0.49 depending on the business category. The study analyzes Yelp data on over 500,000 restaurant reviews to validate the model, finding it tends to slightly underestimate reviews receiving larger numbers of useful votes. To improve predictions, the model is trained and applied separately to each of 10 major business categories.
Presentation for Information Retrieval / Extraction Project on Yelp Data set. The project utilizes various Information Retrieval and Natural Language Processing concepts to build the models.
This document discusses conjoint analysis and cluster analysis, which are multivariate statistical techniques. Conjoint analysis is used to understand how consumers develop preferences for products/services based on attribute combinations. Cluster analysis groups observations into similar clusters to segment markets for targeting customers. The document provides examples of each technique applied to developing an industrial cleanser and measuring customer loyalty. It explains key concepts for each technique like attributes, profiles, distances, linkages, and determining optimal cluster numbers.
User service-rating-prediction-by-exploring social users rating BehavioursShakas Technologies
This document proposes a user-service rating prediction approach that explores social users' rating behaviors. It focuses on aspects of users' rating behaviors like when they rate items, what the ratings and items are, and how ratings diffuse among social friends. The approach represents rating schedules and models interpersonal rating diffusion. It fuses personal interest factors, interpersonal interest similarity, rating behavior similarity, and rating diffusion into a matrix factorization framework. The proposed approach aims to address limitations of existing collaborative filtering recommender systems like increased computational costs, lack of privacy, and insecure computations.
Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Lea...jianjinshu
This document discusses a trust-based rating prediction approach for recommending content in collaborative learning social software. It presents a 3A interaction model to build users' trust networks based on activities, artifacts, and actors. The approach measures direct and indirect trust to infer trust values implicitly. It then predicts ratings for items by taking the weighted average of ratings from trustable users in one's network. The approach is evaluated on a dataset and shown to improve over simple averaging. Future work involves deploying and evaluating the approach on a collaborative learning platform.
This document describes research conducted to predict star ratings for Yelp reviews using language feature analysis. The researchers used linear regression models to analyze basic readily available features from the Yelp dataset, including business star ratings, user average ratings, and review vote counts. Topic models using latent Dirichlet allocation (LDA) were also analyzed as advanced language features extracted from review texts. Stemming text prior to LDA improved predictive performance compared to the baseline model. The best performing model used a combination of basic features and LDA topic distributions, reducing mean squared error over the baseline.
A Supervised Modeling Approach to Determine Elite Status of Yelp MembersJennifer (Hui) Li
The document describes research conducted by a team of students at Carnegie Mellon University to predict whether a user on the Yelp platform will obtain elite status. The team analyzed attributes of users in the Yelp Academic Dataset to identify correlations with elite status. They found that number of reviews, number of fans, and counts of useful, cool, and funny votes showed significant differences between elite and non-elite users. The team then experimented with various machine learning algorithms like decision trees, k-nearest neighbors, and linear regression to classify users and predict elite status. Their best model achieved a prediction accuracy of 94.2% using a decision tree algorithm.
IRJET- Fake Review Detection using Opinion MiningIRJET Journal
This document summarizes a research paper that aims to develop a method for detecting fake reviews on e-commerce websites. The proposed method uses sentiment analysis and opinion mining techniques to classify reviews as "suspicious", "clear", or "hazy". It first runs reviews through the VADER sentiment analysis tool to assign polarity scores, then calculates vector values based on review length, trigram frequency, and sentiment intensity. Reviews are initially classified using a logic table, with "hazy" reviews undergoing further processing. The results include annotated reviews showing sentiment scores and credibility scores to help users identify trustworthy reviews. Future work could improve the dictionary and sentiment weights to increase accuracy of the classification model.
IRJET- Slant Analysis of Customer Reviews in View of Concealed Markov DisplayIRJET Journal
This document summarizes a research paper that proposes a method for sentiment analysis of customer reviews using a Hidden Markov Model. It first discusses how online retailers receive large numbers of customer reviews for products and how it is difficult to analyze the overall sentiment from all reviews. The proposed method involves using a Hidden Markov Model to analyze each review sentence and determine if it expresses a positive or negative sentiment. The model is trained on a dataset of customer reviews that have been part-of-speech labeled. Experimental results found that the trained Hidden Markov Model achieved high precision and accuracy in classifying the sentiment of reviews.
The document discusses the six main steps for building machine learning models: 1) data access and collection, 2) data preparation and exploration, 3) model build and train, 4) model evaluation, 5) model deployment, and 6) model monitoring. It describes each step in detail, including exploring and cleaning the data, choosing a model type, training the model, evaluating model performance on test data, deploying the trained model, and monitoring the model after deployment. The process is iterative, with steps like data preparation and model training often repeated to improve the model.
Recommender System- Analyzing products by mining Data StreamsIRJET Journal
This document discusses several papers related to recommender systems and analyzing products and reviews. It discusses using data mining techniques like SVM, Naive Bayes and clustering algorithms to build recommendation systems for small businesses based on product sales and reviews. It also discusses detecting fake reviews using language analysis and summarizes papers on using Power BI for data visualization and analyzing research data. Key aspects covered include using data streams to provide recommendations in real-time, detecting fake reviews, using data visualization tools like Power BI for analysis, and combining clustering and association rule mining for recommendations.
Bing Ads' Eric Couch dives in to beginning and advanced Excel tips and tricks for PPC marketers- including data analysis tips, Excel formulas, and incredibly handy plugins.
This document describes a data warehouse and business intelligence project for analyzing Starbucks store data. It discusses extracting data from various structured, semi-structured, and unstructured sources, transforming the data using SQL and R, and loading it into a star schema data warehouse with fact and dimension tables. The data warehouse is then used for business queries and analysis in Tableau, with case studies examining city revenue, visitor and beverage sales by city, and city ratings based on food and beverage counts. The analysis finds that New York City generally has the highest revenue, visitor counts, and ratings.
An E-commerce feedback review mining for a trusted seller’s profile and class...IRJET Journal
This document summarizes research on mining online product reviews to identify fake and authentic feedback and classify sellers based on their trustworthiness. It proposes an algorithm called CommTrust to analyze text feedback comments based on dimensions and weights in order to categorize sellers. The research aims to address the "all good reputation problem" that makes it difficult for customers to identify trustworthy sellers when reputation scores are uniformly high. It discusses using natural language processing and opinion mining techniques on feedback comments to evaluate seller trust profiles.
IRJET- Testing Improvement in Business Intelligence AreaIRJET Journal
1) The document discusses testing techniques in business intelligence and data warehousing. It examines how testing has evolved from an ad hoc process to a more systematic discipline.
2) While research has produced many sound testing methods, few have been successfully applied in industry due to a "testing gap" between research and practice. Methods remain time-consuming and implementations are not well-automated.
3) The paper aims to analyze how testing techniques have matured, barriers to their adoption, and how to better transfer methods to industry use. It focuses on theoretical underpinnings of techniques and how they can be developed into systematic methodologies.
IRJET- Classification of Business Reviews using Sentiment AnalysisIRJET Journal
This document summarizes a research paper that aims to classify business reviews as positive or negative using sentiment analysis and machine learning techniques. It discusses how sentiment analysis has become important for understanding customer opinions. The paper proposes automatically classifying large numbers of customer reviews for businesses using only the text, without manual intervention. It describes preprocessing text reviews, extracting features, and using machine learning algorithms like Naive Bayes and Linear Support Vector Classification to achieve over 90% accuracy in classifying reviews as positive or negative.
Adapting data warehouse architecture to benefit from agile methodologiesTom Breur
This document discusses adapting data warehouse architecture to benefit from agile methodologies. It presents a case study comparing traditional 3NF and dimensional data models to the Data Vault model. The case study shows that traditional models are negatively impacted by changes in requirements over time, increasing costs, while the Data Vault model more gracefully accommodates changes with only linear increases in costs. The document concludes that to fully embrace agile methodologies, data warehouses need to be designed differently using a hyper-normalized approach like Data Vault to avoid accumulating technical debt from changes.
Using topic modeling techniques like supervised latent Dirichlet allocation, the author analyzed over 1.5 million Yelp reviews to predict review star ratings and identify topics that most influence customer satisfaction. Key topics associated with high ratings included friendliness, location, and menu variety, while long wait times, poor food quality, and server mistakes led to lower ratings. The model achieved better prediction than assuming the most common rating, demonstrating the technique's potential to help businesses improve based on customer feedback.
This document summarizes key considerations for evaluating collaborative filtering recommender systems. It discusses the user tasks being evaluated, types of analysis and datasets used, ways to measure prediction quality and other attributes, and how to evaluate the overall system from the user perspective. It presents empirical results showing that different accuracy metrics on one dataset collapsed into three groups that were either strongly or uncorrelated. The document aims to help researchers and practitioners properly evaluate and compare recommender system algorithms.
E-commerce giants design and run frequent campaigns on their touchpoints which also includes websites to attract more and more customers. The purpose of this paper is to investigate the effectiveness of a newly launched web page for consumers and find out if the new page is resulting in different consumer behavior and/or more website visits and conversion. The ‘Chi-Square Test of Independence’ helps us find out if the different user groups of old and new web page are significantly different from each other based on conversion rate or not!
The document proposes a method to recommend users on Q&A sites who are most likely to correctly answer questions. It involves:
1) Classifying questions into tags using logistic regression and SVM models trained on historical data.
2) Calculating a weighted score for each user based on past answer performance for each tag.
3) Recommending top users for tags identified in step 1 as most likely to answer new questions correctly. Experimental results showed this approach worked better for common tags with more training data, while rare tags remained inaccurate to classify. Future work is needed to improve recommendations and user experience.
IRJET- Product Aspect Ranking and its ApplicationIRJET Journal
The document presents a framework for product aspect ranking using consumer reviews from online sources. It aims to identify important aspects of products by extracting aspects from reviews, classifying the sentiment on each aspect, and ranking aspects based on frequency and influence on overall consumer sentiment. The framework includes data preprocessing of reviews, aspect identification by extracting frequent nouns, sentiment classification of reviews as positive, negative or neutral, and a probabilistic ranking algorithm to determine important aspects. It is proposed that identifying and ranking important product aspects can help consumers make purchase decisions and help companies improve products. The framework is implemented and evaluated on consumer reviews from various sources and products.
Computing Ratings and Rankings by Mining Feedback CommentsIRJET Journal
This document presents a framework for computing ratings and rankings of sellers on e-commerce platforms by mining feedback comments. It aims to address the issue of "all good reputation" where feedback is overwhelmingly positive. The proposed approach uses text mining techniques like opinion mining and sentiment analysis on feedback comments to extract aspect ratings for different dimensions of transactions. A calculation is proposed using dependency analysis and Latent Dirichlet Allocation to cluster aspect expressions into dimensions and compute dimension ratings and weights. Testing on eBay and Amazon data shows this approach can better distinguish sellers by reducing positive bias compared to existing reputation systems.
- Mariska Hargitay is an American actress known for her role as Olivia Benson on Law & Order: Special Victims Unit.
- She has used her celebrity platform to advocate for victims of sexual assault and help reform laws surrounding the backlog of untested rape kits.
- Through the Joyful Heart Foundation, which she founded, Hargitay has helped pass laws to process untested rape kits and support victims of sexual assault.
This chapter discusses the importance of performance measurement in supply chains. It explains that establishing metrics allows companies to understand how they are performing and identify areas for improvement. Good metrics should be consistent with company strategies and focus on customer needs. The chapter provides examples of different types of metrics companies can use to measure costs, inventory levels, customer service, and overall supply chain performance. These metrics can be classified in various categories and should be integrated both within and across companies to effectively drive improvement.
This chapter discusses the importance of performance measurement in supply chains. It explains that establishing metrics allows companies to understand how they are performing and identify areas for improvement. Good metrics should be consistent with company strategies and focus on customer needs. The chapter provides examples of different types of metrics companies can use to measure costs, inventory levels, customer service, and overall supply chain performance. These metrics can be classified in various categories and should be integrated both within and across companies to effectively drive improvement.
The document discusses the process of data preparation for analysis. It involves checking data for accuracy, developing a database structure, entering data into the computer, and transforming data. Key steps include logging incoming data, screening for errors, generating a codebook to document the database structure and variables, entering data using double entry to ensure accuracy, and transforming data through handling missing values, reversing items, calculating scale totals, and collapsing variables into categories.
IRJET- Slant Analysis of Customer Reviews in View of Concealed Markov DisplayIRJET Journal
This document summarizes a research paper that proposes a method for sentiment analysis of customer reviews using a Hidden Markov Model. It first discusses how online retailers receive large numbers of customer reviews for products and how it is difficult to analyze the overall sentiment from all reviews. The proposed method involves using a Hidden Markov Model to analyze each review sentence and determine if it expresses a positive or negative sentiment. The model is trained on a dataset of customer reviews that have been part-of-speech labeled. Experimental results found that the trained Hidden Markov Model achieved high precision and accuracy in classifying the sentiment of reviews.
The document discusses the six main steps for building machine learning models: 1) data access and collection, 2) data preparation and exploration, 3) model build and train, 4) model evaluation, 5) model deployment, and 6) model monitoring. It describes each step in detail, including exploring and cleaning the data, choosing a model type, training the model, evaluating model performance on test data, deploying the trained model, and monitoring the model after deployment. The process is iterative, with steps like data preparation and model training often repeated to improve the model.
Recommender System- Analyzing products by mining Data StreamsIRJET Journal
This document discusses several papers related to recommender systems and analyzing products and reviews. It discusses using data mining techniques like SVM, Naive Bayes and clustering algorithms to build recommendation systems for small businesses based on product sales and reviews. It also discusses detecting fake reviews using language analysis and summarizes papers on using Power BI for data visualization and analyzing research data. Key aspects covered include using data streams to provide recommendations in real-time, detecting fake reviews, using data visualization tools like Power BI for analysis, and combining clustering and association rule mining for recommendations.
Bing Ads' Eric Couch dives in to beginning and advanced Excel tips and tricks for PPC marketers- including data analysis tips, Excel formulas, and incredibly handy plugins.
This document describes a data warehouse and business intelligence project for analyzing Starbucks store data. It discusses extracting data from various structured, semi-structured, and unstructured sources, transforming the data using SQL and R, and loading it into a star schema data warehouse with fact and dimension tables. The data warehouse is then used for business queries and analysis in Tableau, with case studies examining city revenue, visitor and beverage sales by city, and city ratings based on food and beverage counts. The analysis finds that New York City generally has the highest revenue, visitor counts, and ratings.
An E-commerce feedback review mining for a trusted seller’s profile and class...IRJET Journal
This document summarizes research on mining online product reviews to identify fake and authentic feedback and classify sellers based on their trustworthiness. It proposes an algorithm called CommTrust to analyze text feedback comments based on dimensions and weights in order to categorize sellers. The research aims to address the "all good reputation problem" that makes it difficult for customers to identify trustworthy sellers when reputation scores are uniformly high. It discusses using natural language processing and opinion mining techniques on feedback comments to evaluate seller trust profiles.
IRJET- Testing Improvement in Business Intelligence AreaIRJET Journal
1) The document discusses testing techniques in business intelligence and data warehousing. It examines how testing has evolved from an ad hoc process to a more systematic discipline.
2) While research has produced many sound testing methods, few have been successfully applied in industry due to a "testing gap" between research and practice. Methods remain time-consuming and implementations are not well-automated.
3) The paper aims to analyze how testing techniques have matured, barriers to their adoption, and how to better transfer methods to industry use. It focuses on theoretical underpinnings of techniques and how they can be developed into systematic methodologies.
IRJET- Classification of Business Reviews using Sentiment AnalysisIRJET Journal
This document summarizes a research paper that aims to classify business reviews as positive or negative using sentiment analysis and machine learning techniques. It discusses how sentiment analysis has become important for understanding customer opinions. The paper proposes automatically classifying large numbers of customer reviews for businesses using only the text, without manual intervention. It describes preprocessing text reviews, extracting features, and using machine learning algorithms like Naive Bayes and Linear Support Vector Classification to achieve over 90% accuracy in classifying reviews as positive or negative.
Adapting data warehouse architecture to benefit from agile methodologiesTom Breur
This document discusses adapting data warehouse architecture to benefit from agile methodologies. It presents a case study comparing traditional 3NF and dimensional data models to the Data Vault model. The case study shows that traditional models are negatively impacted by changes in requirements over time, increasing costs, while the Data Vault model more gracefully accommodates changes with only linear increases in costs. The document concludes that to fully embrace agile methodologies, data warehouses need to be designed differently using a hyper-normalized approach like Data Vault to avoid accumulating technical debt from changes.
Using topic modeling techniques like supervised latent Dirichlet allocation, the author analyzed over 1.5 million Yelp reviews to predict review star ratings and identify topics that most influence customer satisfaction. Key topics associated with high ratings included friendliness, location, and menu variety, while long wait times, poor food quality, and server mistakes led to lower ratings. The model achieved better prediction than assuming the most common rating, demonstrating the technique's potential to help businesses improve based on customer feedback.
This document summarizes key considerations for evaluating collaborative filtering recommender systems. It discusses the user tasks being evaluated, types of analysis and datasets used, ways to measure prediction quality and other attributes, and how to evaluate the overall system from the user perspective. It presents empirical results showing that different accuracy metrics on one dataset collapsed into three groups that were either strongly or uncorrelated. The document aims to help researchers and practitioners properly evaluate and compare recommender system algorithms.
E-commerce giants design and run frequent campaigns on their touchpoints which also includes websites to attract more and more customers. The purpose of this paper is to investigate the effectiveness of a newly launched web page for consumers and find out if the new page is resulting in different consumer behavior and/or more website visits and conversion. The ‘Chi-Square Test of Independence’ helps us find out if the different user groups of old and new web page are significantly different from each other based on conversion rate or not!
The document proposes a method to recommend users on Q&A sites who are most likely to correctly answer questions. It involves:
1) Classifying questions into tags using logistic regression and SVM models trained on historical data.
2) Calculating a weighted score for each user based on past answer performance for each tag.
3) Recommending top users for tags identified in step 1 as most likely to answer new questions correctly. Experimental results showed this approach worked better for common tags with more training data, while rare tags remained inaccurate to classify. Future work is needed to improve recommendations and user experience.
IRJET- Product Aspect Ranking and its ApplicationIRJET Journal
The document presents a framework for product aspect ranking using consumer reviews from online sources. It aims to identify important aspects of products by extracting aspects from reviews, classifying the sentiment on each aspect, and ranking aspects based on frequency and influence on overall consumer sentiment. The framework includes data preprocessing of reviews, aspect identification by extracting frequent nouns, sentiment classification of reviews as positive, negative or neutral, and a probabilistic ranking algorithm to determine important aspects. It is proposed that identifying and ranking important product aspects can help consumers make purchase decisions and help companies improve products. The framework is implemented and evaluated on consumer reviews from various sources and products.
Computing Ratings and Rankings by Mining Feedback CommentsIRJET Journal
This document presents a framework for computing ratings and rankings of sellers on e-commerce platforms by mining feedback comments. It aims to address the issue of "all good reputation" where feedback is overwhelmingly positive. The proposed approach uses text mining techniques like opinion mining and sentiment analysis on feedback comments to extract aspect ratings for different dimensions of transactions. A calculation is proposed using dependency analysis and Latent Dirichlet Allocation to cluster aspect expressions into dimensions and compute dimension ratings and weights. Testing on eBay and Amazon data shows this approach can better distinguish sellers by reducing positive bias compared to existing reputation systems.
- Mariska Hargitay is an American actress known for her role as Olivia Benson on Law & Order: Special Victims Unit.
- She has used her celebrity platform to advocate for victims of sexual assault and help reform laws surrounding the backlog of untested rape kits.
- Through the Joyful Heart Foundation, which she founded, Hargitay has helped pass laws to process untested rape kits and support victims of sexual assault.
This chapter discusses the importance of performance measurement in supply chains. It explains that establishing metrics allows companies to understand how they are performing and identify areas for improvement. Good metrics should be consistent with company strategies and focus on customer needs. The chapter provides examples of different types of metrics companies can use to measure costs, inventory levels, customer service, and overall supply chain performance. These metrics can be classified in various categories and should be integrated both within and across companies to effectively drive improvement.
This chapter discusses the importance of performance measurement in supply chains. It explains that establishing metrics allows companies to understand how they are performing and identify areas for improvement. Good metrics should be consistent with company strategies and focus on customer needs. The chapter provides examples of different types of metrics companies can use to measure costs, inventory levels, customer service, and overall supply chain performance. These metrics can be classified in various categories and should be integrated both within and across companies to effectively drive improvement.
The document discusses the process of data preparation for analysis. It involves checking data for accuracy, developing a database structure, entering data into the computer, and transforming data. Key steps include logging incoming data, screening for errors, generating a codebook to document the database structure and variables, entering data using double entry to ensure accuracy, and transforming data through handling missing values, reversing items, calculating scale totals, and collapsing variables into categories.
2. Prediction of Yelp Rating using Yelp Reviews
Kartik Lunkad
May 12, 2015
Abstract
Yelp provides two main ways for users to
review the businesses – reviews & stars.
Traditionally, businesses have focused on
how their rating to assess whether users
like their service or not. But reviews contain
huge amounts of critical data for the
businesses which they can take advantage
of. In this paper, we explore how reviews
can be used to predict the rating of a
business.
1 Introduction
Recommender systems have come a long
way in terms of modeling ratings for various
purposes such as predicting the future
rating of the product/business, identifying
the customer segment who is most
interested in the product and measuring
the success of a product/business. But
interestingly, very little work has been done
in the field of analyzing the reviews which
are provided by the users. These reviews
should not be ignored since they are a rich
source of information for the businesses.
In this paper, we look at these reviews to
predict the rating of the business. We have
focused on restaurants only for the purpose
of this research. Reviews tend to be biased
based on the users’ thinking of what rating
should be for a restaurant. Reviews can be
extremely variable in length, content and
style. We try to remove this bias by
predicting the rating purely from the
content and style of the reviews.
2 Related Work
There has been some previous work in
extracting information from the user
written reviews. The work began when Yelp
started the Dataset Challenge few years
back.
One work was focused towards identifying
the subtopics in the reviews which are
important to the user other than the quality
of food [1]. They used the online LDA,
generative probabilistic model for
collections of discrete data such as text
corpora.
Another interesting work I found in this
area was personalizing the ratings based on
the different topics extracted across
different user reviews [2]. This was done
using a modified, semantic-driven LDA.
The third work which was closest to this
paper focused on predicting the rating using
sentiment analysis [3]. Their research scope
was focused towards only 1 user and close
to 1000 reviews. This didn’t provide a
holistic approach which has been covered in
this research paper.
3 Data Collection
The data for the project was collected was
provided by Yelp themselves for a Yelp
dataset challenge which is conducted to
provide opportunities to explore a real
world dataset.
3. The size of the dataset itself is in millions of
records, but we have focused on specific
section of restaurants for the purpose of
the project.
4 Procedure Outline
The objective of this paper is to train a
classifier that can predict the rating of a
restaurant from reviews written by users.
This section outlines that process. Data
preparation and feature selection are
outlined in Section 5; this section explores
how the data is brought to a form that can
be used to create the models. This section
also discusses how the data is divided into
development, cross-validation and test sets.
In Section 6, exploratory data analysis is
performed on the development data set.
Feature selection is performed here too.
Section 7 presents a baseline performance,
using Naïve Bayes, SVM Logistic Regression
with default settings on the cross-validation
dataset. Parametric optimization is
performed in Section 8; this includes a
comparison of baseline and optimized
performance. Finally, the optimized model
is trained on cross-validation dataset and
used to classify instances in the test
dataset. The results of this are presented in
Section 9.
5 Data Preparation
I divided the data into three sets:
development, used for data exploration;
the cross-validation dataset and the test
set, to be used after optimization.
We have taken close to 20,000 records of
Yelps’ restaurant data. The development set
has close to 4000 records, cross-validation
has close to 14000 and test set has 2000.
Yelp provides 5 entity types: business,
review, user, check-in & tip. We have
focused on the business and the review
entities for the project.
The business entity contains attributes such
as type, business_id, name, full_address,
city, state, latitude, longitude, stars,
review_count, categories, open, hours etc.
The review entity contains attributes such
as type, business_id, user_id, stars, text,
date & votes.
I identified a list of restaurants from the
business entity and then collected all the
reviews for those restaurants from the
review entity.
Also, I converted the numeric columns into
nominal by mapping 1-5 values to its
equivalent nominal values (one, two, three,
four & five).
The final attribute list for the project were
1. business_id
2. stars: overall average stars
3. review_stars: stars for the particular
review
4. nominal_stars
5. nominal_review_stars
6. review_id
We focus on predicting the
nominal_review_stars from the model we
build.
6 Baseline Performance
I performed a baseline analysis using some
modified LightSide settings. The rare
threshold was 25 and the feature selection
was 1000 features. The models discussed in
4. this section, were trained and tested on the
cross-validation dataset.
I first used Naïve Bayes which is a
probabilistic learning method.
Naïve Bayes SVM Logistic Regression
Accuracy 0.4168 0.5235 0.5313
Kappa 0.2209 0.3506 0.3583
Table 1: Baseline Performance
Then, I ran Support Vector Machine (SVM)
as the model, another supervised learning
method
The last model I ran was Logistic Regression.
Among the three models, SVM and Logistic
Regression had comparable performances
with Logistic Regression being slightly
better.
I then used the cross-validation dataset as
my training set and my development
dataset as my test set using Logistic
Regression as my model.
I performed the error analysis for that
which I will talk about in the next section.
7 Data Exploration
I now used the development dataset for
testing the trained model and also to
perform the error analysis.
There were two important considerations I
noticed in the error analysis of the
development dataset.
First, I identified features which had a low
vertical absolute difference, high feature
weight and high frequency between
different classes. But this was not useful,
since four and five, one and two, two and
three etc. had too much similarity and were
hard to distinguish. I then focused on pairs
such as five & three, one & three etc.
There were two interesting results I felt
from the error analysis. One, a good
number of the features were adjectives. An
extremely high feature influence was
exclamation. Exclamation explained the
users’ excitement regarding a restaurant –
positive and negative.
The other aspect I noticed was the
distribution of weights among all the
features was disperse. This made me
speculate whether identifying the top K
features would affect the model’s accuracy
or not.
I tried three feature engineering options
with SVM & Logistic Regression. The first
feature engineering option was to attempt
using word/POS pairs and other was to use
stretchy patterns with the POS adjectives as
a category. I’ve provided the Kappa values
for the efforts below.
5. Baseline Word/POS pairs POS adjectives
Performance Kappa Accuracy Kappa Accuracy Kappa Accuracy
SVM 0.3249 0.5341 0.347 0.5211 0.3474 0.5213
Logistic Regression 0.3305 0.5399 0.353 0.5275 0.353 0.5275
Table 2: Feature Engineering Comparison
From the table, we can see that there’s a
distinct performance improvement with
POS pairs and POS adjectives over Baseline.
The third one was to try out the different
feature selection numbers: 50, 100, 250,
500 & 1000. I’ve tabularized the results for
both the feature engineering efforts. This
table contains Kappa values only.
Top k features 50 100 250 500 1000
SVM 0.2768 0.3068 0.3408 0.3591 0.3474
Logistic Regression 0.2828 0.3115 0.345 0.3579 0.353
Table 3: Feature Selection Performance Comparison
From the table, we can see that the
performance improves with increasing the
number of features till 500, but after that it
starts decreasing. I’ll discuss the results of
these efforts in detail in Section 9.
Next, we will look at how we can
optimize/tune the model better to improve
the performance and accuracy for Logistic
Regression.
8 Optimization
In terms of tuning the performance for
Logistic Regression, there are three main
options are L2 Regularization, L1
Regularization & L2 Regularization (Dual).
Below is a table showing the performance
of the different options.
Accuracy Kappa
L2 Regularization 0.5336 0.3579
L1 Regularization 0.5374 0.3625
L2 Regularization 0.5336 0.3579
Table 4: Logistic Regression Tuning
We can see that L1 Regularization has a
distinct optimization performance
improvement over the other two options.
9 Results
From the different feature engineering and
model optimization, I have come across
some interesting findings.
First, POS pairs/adjectives are better
features than just unigrams themselves.
6. The reasoning behind this is that POS pairs
focus on parts of speech rather than just
the words themselves. Also, adjectives
(positive or negative) have a strong
influence over the rating since they indicate
the sentiment of the user.
The second finding is that 500 features is an
optimal number for selecting the top k
features before the model is trained. This
makes sense as too many features lower
the weights for some of the important
features, whereas too few end up removing
some of the important features.
Logistic Regression and SVM have similar
performances but Logistic Regression
performs slightly better when the model is
tuned.
10 Future Work
In this paper, I have focused on identifying
the features which influence the rating and
also the model which performs based for
predicting the rating for all the users.
In future, I would like to derive sub-genres
from the reviews which users/people
generally care the most about other than
food. By identifying these sub-genres and
giving them individual rating, we can get an
overall review rating which might be even
more accurate.
The other work I am interested in is to focus
on understanding the different types of
users who provide the rating and the
factors they use to decide a particular rating
of a restaurant.
Finally, I would like to extend the research
to other types of businesses as well.
11 References
[1] J. Huang, S. Rogers and E. Joo, "Improving
Restaurants by Extacting Subtopics from
Yelp Reviews," 2013.
[2] J. Linshi, "Personalizing Yelp Star Ratings: a
Semantic Topic Modeling Approach".
[3] C. Li and J. Zhang, "Prediction of Yelp Review
Star Rating using Sentiment Analysis".