Semantic analysis of travel reviews to understand feelings Sept 17 th , 2007 "Don't cry for me, Google" Let me tell you...I would highly recommend that unless you are a party animal, a heavy drinker I would not let my dog to search at Google My dog is better behaved than most kids I saw in this hotel I ate a 3.5 pound lobster for just $16.99 at the seafood restaurant inside the hotel which is the only thing I felt good about. Boris Galitsky Kango.com

Conventional search engines Deliver documents which include significant occurrences of keywords in user queries. Additionally, Google is good at selecting those documents which has satisfied users in previous similar searches. Powerset is trying to understand linguistic links between words in queries and documents While this works well enough for general horizontal searches, vertical searches for specific kinds of products can do much better. Horizontal (about everything) By using domain knowledge (ontology)

Deliver documents which include significant occurrences of keywords in user queries.

Additionally, Google is good at selecting those documents which has satisfied users in previous similar searches.

Powerset is trying to understand linguistic links between words in queries and documents

While this works well enough for general horizontal searches, vertical searches for specific kinds of products can do much better.

Ontologies for vertical search Computers cannot really “know” what each word “means” However, we can teach them how entities are related to each other In a vertical search, for every word (or entity, denoted by it) there should be coded relationships with other words Then the index for each document contains multiple inter-related entities : skeleton of a document for search Dive in Monterey activity Relationship = ‘performed at’ location dive - boat dive - lesson dive – deep - oxygen

Computers cannot really “know” what each word “means”

However, we can teach them how entities are related to each other

In a vertical search, for every word (or entity, denoted by it) there should be coded relationships with other words

Then the index for each document contains multiple inter-related entities : skeleton of a document for search

To recommend a product It is very convincing to refer to the experience of those who used it before To provide argumentation why this product is good for particular user, having discovered it suited well similar users a search engine must not only "understand" the features of products such as a hotel close to outdoor activities , but also feeling of people about these products like not impressed with a view but nice for guys' getaway . It is not a recommendation opposite is understood

It is very convincing to refer to the experience of those who used it before

To provide argumentation why this product is good for particular user, having discovered it suited well similar users

To handle recommendation queries a search engine must know that: hotels are characterized with locations, sometimes good locations are those which are close to activities ( in particular, outdoor activities). “ views” are important considerations while staying in hotels, expression "not impressed" refers to a negative feeling, which is nevertheless combined with positive reference to the category such as "guys getaway".

a search engine must know that:

hotels are characterized with locations,

sometimes good locations are those which are close to activities ( in particular, outdoor activities).

“ views” are important considerations while staying in hotels,

expression "not impressed" refers to a negative feeling, which is nevertheless combined with positive reference to the category such as "guys getaway".

Understanding sarcastic expressions It is also necessary to understand sarcastic expressions like: Not clean => Dogs are not allowed =>

It is also necessary to understand sarcastic expressions like:

Technology: using semantic templates

A traveler profile Confidential - Contains Trade Secrets

Positive and negative sentiments Confidential - Contains Trade Secrets

Overview: we understand facts and feelings about travel products and pages Why: we know how to link the underlying facts and consumer feelings about a product to return recommendations relevant to the consumers’ explicit and implicit intentions Example: Portola Plaza is the top family hotel because it is rated highly: Based on quantifiable facts - it has a pool with a slide, kids eat and stay free and it has an endorsement from parenting magazine. It also has kitchenettes and babysitting available Based on the feelings about the hotel -15% of reviews mentioned the children enjoyed it e.g. “great little indoor pool and jacuzzi which my kids loved”, “family friendly, easy access to state parks”, “right on the beach, very family friendly” and “staff was attentive to my kids” How - based on our ontology, we extracted facts & feelings about products: Used natural language processing to extract consumers and experts feelings from reviews, blogs and articles Applied rule-based reasoning to derive data and meta-data from underlying facts and match them against consumers intentions and themes Mined guidebooks and other offline sources for facts and feelings not online Created a search relevancy algorithm that marries the extracts of feelings, the derived data, and facts to match them against the consumer’s intention and theme-based intentions Why our results are better: Ontology required to represent facts and feelings cannot be scaled up for horizontal search Most vertical searches do not integrate facts and feelings into the form which can be matched against user queries

Why: we know how to link the underlying facts and consumer feelings about a product to return recommendations relevant to the consumers’ explicit and implicit intentions

Example: Portola Plaza is the top family hotel because it is rated highly:

Based on quantifiable facts - it has a pool with a slide, kids eat and stay free and it has an endorsement from parenting magazine. It also has kitchenettes and babysitting available

Based on the feelings about the hotel -15% of reviews mentioned the children enjoyed it e.g. “great little indoor pool and jacuzzi which my kids loved”, “family friendly, easy access to state parks”, “right on the beach, very family friendly” and “staff was attentive to my kids”

How - based on our ontology, we extracted facts & feelings about products:

Used natural language processing to extract consumers and experts feelings from reviews, blogs and articles

Applied rule-based reasoning to derive data and meta-data from underlying facts and match them against consumers intentions and themes

Mined guidebooks and other offline sources for facts and feelings not online

Created a search relevancy algorithm that marries the extracts of feelings, the derived data, and facts to match them against the consumer’s intention and theme-based intentions

Why our results are better:

Ontology required to represent facts and feelings cannot be scaled up for horizontal search

Most vertical searches do not integrate facts and feelings into the form which can be matched against user queries