Sentiment Analysis of Entity (Entity-level Sentiment Analysis)

Question

I've been working on document level sentiment analysis since past 1 year. Document level sentiment analysis provides the sentiment of the complete document. For example - The text "Nokia is good but vodafone sucks big time" would have a negative polarity associated with it as it would be agnostic to the entities Nokia and Vodafone. How would it be possible to get entity level sentiment, like positive for Nokia but negative for Vodafone ? Are there any research papers providing a solution to such problems ?

Answer 1

You can try Aspect-level or Entity-level Sentiment Analysis. There are good efforts have been already done to find the opinions about the aspects in a sentence. You can find some of works here. You can also go further and deeper and review those papers that are related to feature (aspect) extraction. What does it mean? Let me give you an example:

"The quality of screen is great, however, the battery life is short."

Document-level sentiment analysis may not give us the real sense of this document here because we have one positive and one negative sentence in the document. However, by aspect-based (aspect-level) opinion mining, we can figure out the senses/polarities towards different entities in the document separately. By doing feature extraction, in the first step, you try to find the features (aspects) in different sentences (in here "quality of screen" or simply "quality" and "battery life"). Afterwards, when you have these aspects, you try to extract opinions related to these aspects ("great" for "quality" and "short" for "battery life"). In researches and academic papers, we also name features (aspects) as target words (those words or entities on which users comment), and the opinions as opinion words, the comments that have been stated about the target words.

By searching the keywords that I have just mentioned, you can become more familiar with these concepts.

Answer 2

You could look for entities and their coreferents, and have a simple heuristic like giving each entity sentiment from the closest sentiment term, perhaps closest by distance in a dependency parse tree, as opposed to linearly. Each of those steps seems to be an open research topic.

http://scholar.google.com/scholar?q=entity+identification

http://scholar.google.com/scholar?q=coreference+resolution

http://scholar.google.com/scholar?q=sentiment+phrase

http://scholar.google.com/scholar?q=dependency+parsing