DescribeYourBar

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Eric Carlson

A natural language search engine for bars.

Software

Describe Your Bar
Context Sensitive Natural Language Search
and Exploration through Topic Space
Eric Carlson

“I want to eat oysters with a view of the bay”
Yelp
We don’t write or think in keywords
Semantic structure is lost
We want to find interesting
neighborhoods and their
relevant topics
Explore
Search
“the bay” vs “bay oyster”

Yelp: “No results in San Francisco”
“I want a club with neon
hoola-hoopers and a dj”
DescribeYourBar Top Result: “Mighty”

500k Raw Yelp
Reviews
Noun Chunked
Tokenized
Lemmatized
Bar & neighborhood topics
extracted from review text
Fast search via
cosine similarity
Doc2Vec
Dense
Capture specific meaning
and word ordering
LDA
Sparse
Capture Interpretable Topics

Validation
Search using held out reviews->Find rank of real business
Correct business is in top 10 results 60% of the time
Top10
60%

“I want oysters with a view of the bay”
DescribeYourBar Yelp

Choosing LDA Parameters
Leveling off of perplexity implies
additional topics don’t help much
#bitsto
predictword
Dirichlet(𝛼) ~ Beta(𝛼, 𝛽 = 1)
𝛼 controls sparsity of topics
Tune to have ~5 top/bar
Topic 1 Topic 2
Number of Topics

Validation
N>1000 bars in SF.
Validate on held out reviews (some very short).
50% of the time, true bar is in top 10!
50%
Top10

Validation
Search rank of true business for each held out review
50% of the time, true business is in top 10!
Top 10

Extensions for the next week
1. Tune LDA sparsity for 4-5 topics/bar
2. Incorporate doc2vec similarity with some weight
add sentiment analysis
3. Display results on map
4. Validate by predicting business of held-out
reviews
5. Segment users via LDA+doc2vec trained on user
concatenated reviews
6. User rating predcition.
Pick out bars that are more interesting to a
given user than the average user

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DescribeYourBar

1. Describe Your Bar Context Sensitive Natural Language Search and Exploration through Topic Space Eric Carlson

2. “I want to eat oysters with a view of the bay” Yelp We don’t write or think in keywords Semantic structure is lost We want to find interesting neighborhoods and their relevant topics Explore Search “the bay” vs “bay oyster”

3. Mission Street Oyster

4. Yelp: “No results in San Francisco” “I want a club with neon hoola-hoopers and a dj” DescribeYourBar Top Result: “Mighty”

5. 500k Raw Yelp Reviews Noun Chunked Tokenized Lemmatized Bar & neighborhood topics extracted from review text Fast search via cosine similarity Doc2Vec Dense Capture specific meaning and word ordering LDA Sparse Capture Interpretable Topics

6. Validation Search using held out reviews->Find rank of real business Correct business is in top 10 results 60% of the time Top10 60%

7. Eric Carlson Physics PhD UC Santa Cruz

8. “I want oysters with a view of the bay” DescribeYourBar Yelp

10. Choosing LDA Parameters Leveling off of perplexity implies additional topics don’t help much #bitsto predictword Dirichlet(𝛼) ~ Beta(𝛼, 𝛽 = 1) 𝛼 controls sparsity of topics Tune to have ~5 top/bar Topic 1 Topic 2 Number of Topics

11.

12. Validation N>1000 bars in SF. Validate on held out reviews (some very short). 50% of the time, true bar is in top 10! 50% Top10

13. Validation Search rank of true business for each held out review 50% of the time, true business is in top 10! Top 10

14.

15. Extensions for the next week 1. Tune LDA sparsity for 4-5 topics/bar 2. Incorporate doc2vec similarity with some weight add sentiment analysis 3. Display results on map 4. Validate by predicting business of held-out reviews 5. Segment users via LDA+doc2vec trained on user concatenated reviews 6. User rating predcition. Pick out bars that are more interesting to a given user than the average user

DescribeYourBar

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