<p>I also tried getting research articles on this but haven't found any. I would suggest you to try using the aspect based sentiment analysis algorithms. The similarity i found is there we recognize aspects of a single entity in a sentence and then find the sentiment of each aspect.Similarly we can train our model using the same algorithm which can detect the entities as it does for aspects and find the sentiment of such entities. I didn't try this but I am going to.Let me know if this worked or not.Also there are various ways to do this. The following are the links for few articles.</p>

<p><a href="http://arxiv.org/pdf/1605.08900v1.pdf" rel="nofollow">http://arxiv.org/pdf/1605.08900v1.pdf</a>
<a href="https://cs224d.stanford.edu/reports/MarxElliot.pdf" rel="nofollow">https://cs224d.stanford.edu/reports/MarxElliot.pdf</a></p>
Sentiment Analysis of Entity (Entity-level Sentiment Analysis)
<p>I've been working on document level sentiment analysis since past 1 year. <em>Document level sentiment analysis</em> provides the sentiment of the complete document. For example - The text "<em>Nokia is good but vodafone sucks big time</em>" would have a negative polarity associated with it as it would be agnostic to the entities Nokia and Vodafone. <em>How would it be possible to get entity level sentiment, like positive for Nokia but negative for Vodafone</em> ? Are there any research papers providing a solution to such problems ?</p>