Ford Motor Company Limited

Yao Ge

John William Schmotzer

Zhen Jiang

Yan Fu

Pol Llado

Taylor Hawley

Xianfeng Hu

Jian Fang

The present disclosure is directed toward a method and system for deconstructing and searching binary based vehicular data. The method includes extracting a vehicle filename metadata for one or more vehicular files; parsing data of the vehicular files to generate categorized objects based on a vehicular data dictionary for the vehicular files; associating each of the categorized objects with a respective vehicle filename metadata; storing vehicular documents indicative of the categorized objects with associated vehicle filename metadata in a database system; building a query based on a search input acquired by way of a search interface being displayed; and transmitting search results in response to the search input. The vehicular data dictionary defines multiple terms indicative of decoding logic provided in the vehicular files.

Method and system for deconstructing and searching binary based vehicular data

User generated content, particularly Chinese language content, is retrieved from various sources such as forums, microblogs, social media sites, and the like. A portion of the content is manually labeled with a sentiment associated with the content and may be classified according to subject matter referenced. Sentiment-indicating features of the content is extracted according to a sentiment dictionary, which may include topic-specific jargon. The features are used to train a classifier to determine sentiment of content based on sentiment-indicating features. The sentiment for other content may then be determined using the classifier. The output of the classifier may be combined with an explicit rating of a product or product feature.

Sentiment analysis of product reviews from social media

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Northville, MI, United States of America

Yao Ge