Sentiment analysis
 Sentiment analysis that delivers more than just positive or negative valuations with built-in sentiment scoring, topic identification and categorisation.
 The purpose of this service would be to extract opinions from text. An impression represents the topic an author is writing about and a sentiment score that classifies how positively or negatively the author feels towards that subject. Deep Linguistic Analysis can be used to identify the subject the author is discussing. This could be:
 ? an entity (brand/ person/product/place?
 ? a thought (like ?global warming?, ?public policies? or ?financial meltdown?).
 The sentiment analysis service will also break the opinion down to detect exactly which features or attributes or elements of the subject are increasingly being discussed. For a product this may be the main components or accessories as for example, the ?screen? in ?the screen of the Galaxy Tab? or the ?case? in ?my new iPad case?. For an individual this could be the activities or attitudes associated with them. For a place maybe it's the specific buildings or institutions located there.
 When combined with our categorisation service these features or attributes can be used to place the opinion in a category taken from a taxonomy. This provides a powerful way to structure a couple of texts according to what topics folks are discussing and how they feel about those topics.
 Sentiment scores are also based on Deep Linguistic Analysis. The more intense the feelings of the author about the subject, the higher or lower the score. To do this, the analysis detects linguistic features like the strength of the vocabulary or the use of intensifiers like ?really?, ?very? or ?extremely?. So a comment like ?Installing software on this machine is painful!? will be scored as less negative than ?Installing software with this machine is really very painful indeed!?
 
 Deep Linguistic Analysis accurately handles complex issues like negation: ?the new Nikon is really not bad at all?.
 The service handles complex linguistic issues that play a significant role in sentiment analysis, such as negation or comparative sentences. Deep Linguistic Analysis automatically handles this kind of phenomena capturing the difference between opinions like:
 
 ? ?This phone is way better than my old phone.? ? Positive
 ? ? https://talee.co.uk/ is not much better than my old phone.? ? Negative
 The sentiment analysis service is not limited to extracting an individual opinion per sentence. It actually detects as much opinions as the sentence contains. For instance in the sentence ?This phone is awesome, but it was much too expensive and the screen is not big enough? three opinions will be extracted: ?phone? + ?awesome?, ?phone? + ?much too expensive? and ?screen? + ?not big enough?.