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 ; name.surname@he-arc.ch +University of Neuchâtel ; name.surname@unine.ch 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.