Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics 2017
DOI: 10.1145/3152178.3152192
|View full text |Cite
|
Sign up to set email alerts
|

Predictive Analytics Using Text Classification for Restaurant Inspections

Abstract: According to the Center for Disease Control (CDC), there are almost 48 million people affected by foodborne diseases in the U.S. every year, including 3,000 deaths. The most effective way of avoiding food poisoning would be its prevention. However, complete prevention is not possible, therefore Public Health departments perform routine restaurant inspections, combined with the practice of inspecting specific restaurants once a disease outbreak is identified. Following other health applications (e.g., predictio… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
3
0

Year Published

2017
2017
2021
2021

Publication Types

Select...
2
1
1

Relationship

2
2

Authors

Journals

citations
Cited by 4 publications
(3 citation statements)
references
References 15 publications
0
3
0
Order By: Relevance
“…Sadilek et al [15] and Wang et al [21] perform studies to detect and predict foodborne illness using respectively Twitter and Yelp. They analyze the tweets using language features and language models.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Sadilek et al [15] and Wang et al [21] perform studies to detect and predict foodborne illness using respectively Twitter and Yelp. They analyze the tweets using language features and language models.…”
Section: Related Workmentioning
confidence: 99%
“…In this paper our focus is on Twitter. In fact, there is a wealth of millions of tweets with different times, locations, and users, in what constitutes a stream of snapshots throughout the pandemic, which we analyze in this paper, following the lead of so many others who have analyzed tweets in a variety of domains [3,4,7,9,10,12,13,15,16,21,23].…”
mentioning
confidence: 99%
“…We are also currently working on performing predictive analytics using text classification for restaurant inspections based on Yelp reviews. This approach extracts features from existing Yelp reviews and uses various machine learning algorithms such as SVM, RNN, Naive Bayes, and Random Forest to identify restaurants that require immediate inspections [27].…”
Section: Related Workmentioning
confidence: 99%