Microsoft Technology Licensing, LLC

Steven J Hanks

Zheng Chen

Jun Yan

Ning Liu

Lei Ji

Qing Xu

Ian A McAllister

Christoph Ponath

Ling Bao

Embodiments facilitate greater flexibility in definition of user segments for targeted advertising, by employing indexed semantic user profiles. Semantic user profiles are built through extraction of online user behavior data such as user search queries and page views, and include user interest information that is inferred based on user behavior. Semantic user profiles are then indexed to facilitate search for a set of users that fit specified semantic search terms. Search results for semantic profiles are ranked according to a ranking model developed through machine learning. In some embodiments, building and indexing of semantic profiles and learning of the ranking model is performed offline to facilitate more efficient online processing of queries.

Semantic user profiles for targeting user segments

Indexing semantic user profiles for targeted advertising

Methods and systems for extraction and summarization of sentiment information related to a particular research subject are disclosed. A method includes accessing sources of information that contain sentiment information that is related to the research subject and extracting the sentiment information from the sources of information as opinions related to the research subject. Opinion categories related to features of the research subject are identified. From this information a summarization of the sentiment information that is related to the particular research subject that includes the identified opinion categories is generated. Subsequently, access is provided to the summarization for graphical presentation.

Extraction and summarization of sentiment information

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Redmond, WA, United States of America

Steven J Hanks