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	<title>Comments on: A Practical Sentiment Analysis Alternative For Social Media</title>
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	<link>http://brandsavant.com/a-practical-sentiment-analysis-alternative-for-social-media/</link>
	<description>Gaining Insight From Social Media Data</description>
	<lastBuildDate>Thu, 09 Sep 2010 16:37:01 +0000</lastBuildDate>
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		<title>By: Ian Lyons</title>
		<link>http://brandsavant.com/a-practical-sentiment-analysis-alternative-for-social-media/comment-page-1/#comment-330</link>
		<dc:creator>Ian Lyons</dc:creator>
		<pubDate>Mon, 15 Mar 2010 07:48:33 +0000</pubDate>
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		<description>Hi Tom,

Have to completely agree with this approach - did it recently for a client who put on an event which was - putting it mildly - not well received.  I created a spreadsheet where the columns (23) represented each distinct issue and each row was online content mentioning (300) the event - ranging from articles, tweets, blogs and our own site comments. For each data point I assigned a -3 to +3 sentiment rating. 

When summing vertically, we quickly saw, prioritised the top issues according to our customers and crafted an appropriate response.  I ended up adding a weighting multiplier column for an important media company, influential individual or people who had gone to the trouble of analysing the situation.  A horizontal summing allowed us to prioritise individual responses.

This level of analysis was certainly time consuming but also well beyond the capability of any automated tool I&#039;ve seen.  I feel we owed it to our customers to do it this way because at the end of it, we really did know all the issues inside out.</description>
		<content:encoded><![CDATA[<p>Hi Tom,</p>
<p>Have to completely agree with this approach &#8211; did it recently for a client who put on an event which was &#8211; putting it mildly &#8211; not well received.  I created a spreadsheet where the columns (23) represented each distinct issue and each row was online content mentioning (300) the event &#8211; ranging from articles, tweets, blogs and our own site comments. For each data point I assigned a -3 to +3 sentiment rating. </p>
<p>When summing vertically, we quickly saw, prioritised the top issues according to our customers and crafted an appropriate response.  I ended up adding a weighting multiplier column for an important media company, influential individual or people who had gone to the trouble of analysing the situation.  A horizontal summing allowed us to prioritise individual responses.</p>
<p>This level of analysis was certainly time consuming but also well beyond the capability of any automated tool I&#8217;ve seen.  I feel we owed it to our customers to do it this way because at the end of it, we really did know all the issues inside out.</p>
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		<title>By: David Phillips</title>
		<link>http://brandsavant.com/a-practical-sentiment-analysis-alternative-for-social-media/comment-page-1/#comment-328</link>
		<dc:creator>David Phillips</dc:creator>
		<pubDate>Sun, 14 Mar 2010 17:50:48 +0000</pubDate>
		<guid isPermaLink="false">http://brandsavant.com/?p=483#comment-328</guid>
		<description>Tom, I have been using a semantic engine http://bit.ly/cIB9GB which gives us the key concepts and concept phrases within the texts inside the corpus.
One of the advantages is that it is possible to weight concepts which gives us the option to create perspectives.  This helps to provide that very human of issues the &#039;view from where I stand&#039;.
I have experience of evaluating large numbers of citations and the founder/MD of MediaMeasurement in the UK. 
Getting inter-coder consistency above 85% for all phrases is pretty hard and so both people and machines  are not 100% on the money.</description>
		<content:encoded><![CDATA[<p>Tom, I have been using a semantic engine <a href="http://bit.ly/cIB9GB" rel="nofollow">http://bit.ly/cIB9GB</a> which gives us the key concepts and concept phrases within the texts inside the corpus.<br />
One of the advantages is that it is possible to weight concepts which gives us the option to create perspectives.  This helps to provide that very human of issues the &#8216;view from where I stand&#8217;.<br />
I have experience of evaluating large numbers of citations and the founder/MD of MediaMeasurement in the UK.<br />
Getting inter-coder consistency above 85% for all phrases is pretty hard and so both people and machines  are not 100% on the money.</p>
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