Data Web Scraping and Sentiment Analysis for Yelp Review

Posted on May 30, 2016
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Contributed by Frank Wang. He  is currently in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between April 11th to July 1st, 2016. This post is based on his third class project - Web Scraping, due on the 6th week of the program.

Human language data is one of the most important data and most are free. Internet and social media time and age, people's opinions, reviews, and recommendations have become a valuable resource for political science and businesses. Thanks to modern technologies, we are now able to collect and analyze such data most efficiently. In this project, I scrape the Yelp review data and then applied sentiment analysis to classify documents based on their polarity: the attitude of the writer.

Yelp Data Web Scraping

Yelp review can be download for free. API and keys are required to download all the data. What I am interested are the review, rating score and author’s name. Beautifufsoup is used to scrape the web data and then information is extracted and save to data frame. Data for different category, such as restaurant and park, are save to different folder for analysis.

Data Web Scraping and Sentiment Analysis for Yelp Review

FIG.1 example of the ABC Kitchen Review

Data Web Scraping and Sentiment Analysis for Yelp Review

FIG.2 Author information data (left) and the histogram of the review rating score

Sentiment Data Analysis

Textbold is first used to check the prediction accuracy. Although Texbold provide option to train the data using Naive Bayes, it is very slow. Therefore the pre-trained model is used for simplicity.

FIG.3 shows the predicted score for ABC kitchen, which is scaled to 1-5 to compare the real score. The distribution is about symmetry with long tails in both ends. The predicted average score is 3.6, which is lower than the real score of 4.2. If we divided the score to two categories: positive and negative, the prediction accuracy is about 96%. The prediction accuracy is largely drop (<50%) when 1-5 rating are compared. The predictor is not generous comparing with human being: the 5 star is much lower. Different data set, such as park review, are tried. The overall conclusions are similar.

Data Web Scraping and Sentiment Analysis for Yelp Review


FIG.3 Predicted rating score for restaurant review



FIG.4 Predicted rating score for park review

Training model using real review data

NLTK tool is used in this study. The review data is transformed to a list of dictionary: bag-of-words. A simple unigram model is used for the first try. We use Naïve Bayes to train our data. The data includes about 6000 restaurant reviews. It is important to randomly shuffle the data before use it. The training data and test data are 70% and 30%, respectively. The prediction accuracy on the test data is about 72% with this simple model. While the accuracy drop to 62% with 2000 data. Apparently more data is needed.

Fig. 5 show the most informative words. It is interesting that some “positive” words, such as “greeting”, “politely” , can becomes negative in the most informative words. They are not errors. Since we use unigram model: the sentences are broken into bag of single words.  We will improve this use bigram model in the future.


FIG. 4 Word cloud of most informative words


Web review is scarped. Besides the review text, the pictures can have useful information. We download review photo for future study.

Preliminary sentiment study is done with simple model using Naive Bayes model. The prediction accuracy is about 72%, which is low due to the simple model. TextBlob package gives high accuracy about 96%. We will continue study using better model: word clean, bigram word-bag-model, tf-idf transform, word stemming and cross-validation.

About Author

Frank Wang

Frank (Lanfa) Wang have worked in several research laboratories as a physicist. He has over a decade of experience in modeling and scientific computing and had access to the large supercomputer NERSC. He participated several national/international projects: Japanese...
View all posts by Frank Wang >

Related Articles

Leave a Comment

Google April 12, 2020
Google Please take a look at the web pages we stick to, such as this a single, as it represents our picks from the web.
Google April 4, 2020
Google Here are some links to websites that we link to due to the fact we believe they may be really worth visiting.
hermes picotin handbags faux June 20, 2017
Im grateful for the blog article.Thanks Again. Will read on… hermes picotin handbags faux

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI