{"id":148352,"date":"2003-01-01T00:00:00","date_gmt":"2003-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/sentiment-analyzer-extracting-sentiments-about-a-given-topic-using-natural-language-processing-techniques\/"},"modified":"2018-10-16T21:07:38","modified_gmt":"2018-10-17T04:07:38","slug":"sentiment-analyzer-extracting-sentiments-about-a-given-topic-using-natural-language-processing-techniques","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/sentiment-analyzer-extracting-sentiments-about-a-given-topic-using-natural-language-processing-techniques\/","title":{"rendered":"Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We present Sentiment Analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents. Instead of classifying the sentiment of an entire document about a subject, SA detects all references to the given subject, and determines sentiment in each of the references using natural language processing (NLP) techniques. Our sentiment analysis consists of 1) a topic specific feature term extraction, 2) sentiment extraction, and 3) (subject, sentiment) association by relationship analysis. SA utilizes two linguistic resources for the analysis: the sentiment lexicon and the sentiment pattern database. The performance of the algorithms was verified on online product review articles (&#8220;digital camera&#8221; and &#8220;music&#8221; reviews), and more general documents including general webpages and news articles.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present Sentiment Analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents. Instead of classifying the sentiment of an entire document about a subject, SA detects all references to the given subject, and determines sentiment in each of the references using natural language processing (NLP) techniques. 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