<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
			xmlns:itunes="http://www.itunes.com/dtds/podcast-1.0.dtd"
		>

<channel>
	<title>Gigaom Search &#187; sentiment analysis</title>
	<atom:link href="http://search.gigaom.com/tag/sentiment-analysis/feed/" rel="self" type="application/rss+xml" />
	<link>http://search.gigaom.com</link>
	<description>Search and browse the archives</description>
	<lastBuildDate>Wed, 11 Mar 2015 13:08:37 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>hourly</sy:updatePeriod>
	<sy:updateFrequency>1</sy:updateFrequency>
	<generator>http://wordpress.org/?v=4.1</generator>
	<item>
		<title>Luminoso brings its text analysis smarts to streaming data</title>
		<link>http://gigaom.com/2015/02/11/luminoso-brings-its-text-analysis-smarts-to-streaming-data/</link>
		<comments>http://gigaom.com/2015/02/11/luminoso-brings-its-text-analysis-smarts-to-streaming-data/#comments</comments>
		<pubDate>Wed, 11 Feb 2015 13:00:54 +0000</pubDate>
		<dc:creator><![CDATA[Barb Darrow]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Clarabridge]]></category>
		<category><![CDATA[Lexalytics]]></category>
		<category><![CDATA[Luminoso]]></category>
		<category><![CDATA[sentiment analysis]]></category>
		<category><![CDATA[social media feeds]]></category>
		<category><![CDATA[Super Bowl]]></category>
		<category><![CDATA[text analytics]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:FB]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:GOOG]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:INTC]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:FB]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:GOOG]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:INTC]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NYSE:SNE]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NYSE:TWTR]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=914068</guid>
		<description><![CDATA[Luminoso, a sentiment analysis startup with DNA from MIT's Media Lab, says its new product can take consumer feedback from Twitter, Facebook, Google+ and potentially other feeds, and boil it into one&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/02/11/luminoso-brings-its-text-analysis-smarts-to-streaming-data/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>As social media gets quantified, more people use Twitter to trade</title>
		<link>https://gigaom.com/2015/02/03/as-social-media-gets-quantified-more-people-use-twitter-to-trade/</link>
		<comments>https://gigaom.com/2015/02/03/as-social-media-gets-quantified-more-people-use-twitter-to-trade/#comments</comments>
		<pubDate>Tue, 03 Feb 2015 19:50:41 +0000</pubDate>
		<dc:creator><![CDATA[Jeff Roberts]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Bloomberg apps]]></category>
		<category><![CDATA[Claudio Storelli]]></category>
		<category><![CDATA[fintech]]></category>
		<category><![CDATA[iSense]]></category>
		<category><![CDATA[sentiment analysis]]></category>
		<category><![CDATA[Structure Data]]></category>
		<category><![CDATA[Structure:Data]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NYSE:TWTR]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=912092</guid>
		<description><![CDATA[Professional investors known as quants use hard facts about companies --  share price, EBITDA, and so on -- to inform the algorithms that carry out their automated trading strategies. But softer sources&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2015/02/03/as-social-media-gets-quantified-more-people-use-twitter-to-trade/feed/</wfw:commentRss>
		<slash:comments>1</slash:comments>
		</item>
		<item>
		<title>Scientists say tweets predict heart disease and community health</title>
		<link>https://gigaom.com/2015/01/22/scientists-say-tweets-predict-heart-disease-and-community-health/</link>
		<comments>https://gigaom.com/2015/01/22/scientists-say-tweets-predict-heart-disease-and-community-health/#comments</comments>
		<pubDate>Thu, 22 Jan 2015 22:15:18 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[public health]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[sentiment analysis]]></category>
		<category><![CDATA[Structure Data 2015]]></category>
		<category><![CDATA[text analysis]]></category>
		<category><![CDATA[Twitter data]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NYSE:TWTR]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=908901</guid>
		<description><![CDATA[University of Pennsylvania researchers have found that the words people use on Twitter can help predict the rate of heart disease deaths in the counties where they live. Places where people tweet happier&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2015/01/22/scientists-say-tweets-predict-heart-disease-and-community-health/feed/</wfw:commentRss>
		<slash:comments>4</slash:comments>
		</item>
		<item>
		<title>BuzzFeed&#8217;s deal with Facebook to measure political sentiment has one major flaw</title>
		<link>https://gigaom.com/2014/11/10/buzzfeeds-deal-with-facebook-to-measure-political-sentiment-has-one-major-flaw/</link>
		<comments>https://gigaom.com/2014/11/10/buzzfeeds-deal-with-facebook-to-measure-political-sentiment-has-one-major-flaw/#comments</comments>
		<pubDate>Mon, 10 Nov 2014 19:10:57 +0000</pubDate>
		<dc:creator><![CDATA[Mathew Ingram]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Politics]]></category>
		<category><![CDATA[sentiment analysis]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:FB]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:FB]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=887636</guid>
		<description><![CDATA[BuzzFeed has formed a partnership with Facebook that gives it access to the social network's "sentiment analysis" data on millions of users -- but Facebook's algorithm is going to influence the&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2014/11/10/buzzfeeds-deal-with-facebook-to-measure-political-sentiment-has-one-major-flaw/feed/</wfw:commentRss>
		<slash:comments>1</slash:comments>
		</item>
		<item>
		<title>CrowdFlower raises $12.5M to deliver better data for better models</title>
		<link>https://gigaom.com/2014/09/17/crowdflower-raises-12-5m-to-deliver-better-data-for-better-models/</link>
		<comments>https://gigaom.com/2014/09/17/crowdflower-raises-12-5m-to-deliver-better-data-for-better-models/#comments</comments>
		<pubDate>Wed, 17 Sep 2014 16:42:38 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[crowdsourcing]]></category>
		<category><![CDATA[data cleaning]]></category>
		<category><![CDATA[data science]]></category>
		<category><![CDATA[sentiment analysis]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=873873</guid>
		<description><![CDATA[Crowdsourcing startup CrowdFlower has raised another $12.5 million as it tries to make life better for the data science community. As people try to get better, faster data to power their&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2014/09/17/crowdflower-raises-12-5m-to-deliver-better-data-for-better-models/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Why we&#8217;re all so obsessed with deep learning</title>
		<link>https://gigaom.com/2014/06/08/why-were-all-so-obsessed-with-deep-learning/</link>
		<comments>https://gigaom.com/2014/06/08/why-were-all-so-obsessed-with-deep-learning/#comments</comments>
		<pubDate>Sun, 08 Jun 2014 15:00:00 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[sentiment analysis]]></category>
		<category><![CDATA[text analysis]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:FB]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:MSFT]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:FB]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:GOOG]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:MSFT]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NYSE:TWTR]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=847799</guid>
		<description><![CDATA[Deep learning is all the rage among the tech scene right now, and that's more a result of its utility than because it sounds cool. Some questioned the feasibility of the&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2014/06/08/why-were-all-so-obsessed-with-deep-learning/feed/</wfw:commentRss>
		<slash:comments>3</slash:comments>
		</item>
		<item>
		<title>If Facebook&#8217;s data is indicative, Denver is Super Bowl fave</title>
		<link>https://gigaom.com/2014/01/30/if-facebooks-data-is-indicative-denver-should-be-super-bowl-fave/</link>
		<comments>https://gigaom.com/2014/01/30/if-facebooks-data-is-indicative-denver-should-be-super-bowl-fave/#comments</comments>
		<pubDate>Thu, 30 Jan 2014 19:56:21 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[data science]]></category>
		<category><![CDATA[sentiment analysis]]></category>
		<category><![CDATA[Super Bowl]]></category>
		<category><![CDATA[text analysis]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:FB]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:FB]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=806767</guid>
		<description><![CDATA[Facebook's data scientists have crunched the numbers (well, the text) and -- if fan sentiment is any indicator -- it looks like Denver is the Super Bowl favorite. Over the course&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2014/01/30/if-facebooks-data-is-indicative-denver-should-be-super-bowl-fave/feed/</wfw:commentRss>
		<slash:comments>1</slash:comments>
		</item>
		<item>
		<title>etcML: A free, easy and fairly accurate tool for analyzing the text of tweets</title>
		<link>https://gigaom.com/2013/12/11/etcml-a-free-easy-and-fairly-accurate-tool-for-analyzing-the-text-of-tweets/</link>
		<comments>https://gigaom.com/2013/12/11/etcml-a-free-easy-and-fairly-accurate-tool-for-analyzing-the-text-of-tweets/#comments</comments>
		<pubDate>Wed, 11 Dec 2013 15:00:32 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[data science]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[sentiment analysis]]></category>
		<category><![CDATA[text analysis]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NYSE:TWTR]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=722967</guid>
		<description><![CDATA[A group of Stanford machine learning students has created a new service for analyzing and classifying passages of text. But the highlight is an easy-to-use feature for classifying whether tweets are&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2013/12/11/etcml-a-free-easy-and-fairly-accurate-tool-for-analyzing-the-text-of-tweets/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Nielsen to measure Twitter chatter about TV shows</title>
		<link>https://gigaom.com/2013/10/07/nielsen-to-measure-twitter-chatter-about-tv-shows/</link>
		<comments>https://gigaom.com/2013/10/07/nielsen-to-measure-twitter-chatter-about-tv-shows/#comments</comments>
		<pubDate>Mon, 07 Oct 2013 18:02:45 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[sentiment analysis]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NYSE:TWTR]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=702287</guid>
		<description><![CDATA[Execs are talking about measuring tweet volume and the reach of those tweets, but isn't the real value in figuring out what people think? It's not worth touting that 200,000 people&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2013/10/07/nielsen-to-measure-twitter-chatter-about-tv-shows/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>If machines can perform sentiment analysis accurately, how will that change business?</title>
		<link>http://research.gigaom.com/2013/10/if-machines-can-perform-sentiment-analysis-accurately-how-will-that-change-business/</link>
		<comments>http://research.gigaom.com/2013/10/if-machines-can-perform-sentiment-analysis-accurately-how-will-that-change-business/#comments</comments>
		<pubDate>Sun, 06 Oct 2013 21:52:54 +0000</pubDate>
		<dc:creator><![CDATA[Stowe Boyd]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[linguistics]]></category>
		<category><![CDATA[richard socher]]></category>
		<category><![CDATA[semantic analysis]]></category>
		<category><![CDATA[sentiment analysis]]></category>

		<guid isPermaLink="false">http://pro.gigaom.com/?post_type=go_shortpost&#038;p=191029</guid>
		<description><![CDATA[Recent research at Stanford (see Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank, Richard Socher, et al) seems to have advanced sentiment analysis of text to a new level of&#8230;]]></description>
		<wfw:commentRss>http://research.gigaom.com/2013/10/if-machines-can-perform-sentiment-analysis-accurately-how-will-that-change-business/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
	</channel>
</rss>