1. Number of sentences by review Number of words by review
Opinion profile construction from social media
A case of study of restaurant reviews
William Droz*, Hatem Ghorbel*, Martin Hilpert+, Magdalena Punceva* and Mehdy Davary+
*HE-Arc Ingénierie – HES-SO ;
+University of Neuchâtel ;
Abstract
The aim of this project is to analyze restaurant reviews in terms of semantic frames in order to provide an opinion profile that reflects customer satisfaction along several features. We first constructed a ‘restaurant frame’ that contains the features that matter to restaurant reviewers. We secondly conducted a fine-grained sentiment analysis that is sensitive to restaurant features as expressed by customers using term classification. We used Yelp’s Academic Dataset to evaluate our methodology found to perform a recall of 52% and a precision of 67% over a manually labeled excerpt. Finally, we implemented a web-based tool capable of extracting restaurant profiles and summarizing the result to the final user.
Adorable
Aerate
Alluring …
Always
Consistence
Continually …
Ambience
bathroom
children
Conistency
crowded
Custom order
Dresscode
Freestuff
General sent.
Location
Menu items
Parking
Portion size
Price
Quality
Seating
Service
Takeout
Time Tv
We detect terms at the sentence level of the reviews referring to the constructed restaurant features.
We construct word chunk within the sentence for each detected feature.
We compute chunk polarity according to Textblob module updated by empirical restaurant-based polarity words list.
We update the restaurant profile accordingly.
The list of terms for the twenty features describing the restaurant profile as constructed from Yelp.
Yelp Corpus
We developed a web prototype that allows users to search the N closest restaurants from a location and then color them from red to green according to their preferences.
Corpus size : 706’290 reviews
Number of sentences by review
Variance
Mean
Std
Median
61.1
9.41
7.81 7
Number of words by review
Variance
Mean
Std
Median
13837
128
117 94
User searched restaurants near Las Vegas that match for good bathroom
Evaluation of the restaurants frame extraction algorithm with manually labeled test corpus (10 businesses and 260 reviews)
Business_id
Precision
recall
-CIZ...
0.4
0.57
1CfO…
0.71
0.5
44zt…
0.67
0.22
dgGp…
0.79
0.65
lxQ1…
0.88
0.7
kJ2a…
0.75
0.6
LIAF…
0.58
0.44
Oi8l…
0.61
smoG…
0.64
0.45
wMzo…
0.47
Mean
0.52
Business_id
Precision
recall
-CIZ...
0.4
0.67
1CfO…
0.8
44zt… 1
0.2
dgGp…
0.85
lxQ1…
0.81
0.72
kJ2a…
0.58
LIAF…
0.5
Oi8l…
0.61
smoG…
0.75
wMzo…
0.53
Mean
0.59
Business_id
Precision
recall
-CIZ...
1CfO… 1
0.2
44zt…
0.5
0.25
dgGp…
lxQ1…
kJ2a…
LIAF…
0.33
Oi8l…
smoG…
0.17
wMzo…
Mean
0.09
for matching categories, what are the part of goodly polarized as negative.
Do the restaurants have the same polarity and categories?
for matching categories, what are the part of goodly polarized as positive.