Sentiment Analysis is MeaningCloud's solution for performing a detailed multilingual sentiment analysis of texts from different sources.
The text provided is analyzed to determine if it expresses a positive/negative/neutral sentiment; to do this, the local polarity of the different sentences in the text is identified and the relationship between them evaluated, resulting in a global polarity value for the whole text.
Besides polarity at sentence and global level, Sentiment Analysis uses advanced natural language processing techniques to also detect the polarity associated to both entities and concepts in the text. It provides a reference in the relevant sentence and a list of elements detected with the aggregated polarity derived from all their appearances, also taking into account the grammatical structures in which they are contained.
You can use your own resources in the sentiment analysis of entities and concepts by creating a dictionary through our dictionaries customization engine. You can also customize the sentiment analysis for your domain by defining your own sentiment model through our sentiment models customization engine.
Sentiment Analysis also gives the user the possibility of detecting the polarity of user-defined entities and concepts, making the service a flexible tool applicable to any kind of scenario.
Additionally, Sentiment Analysis detects if the text processed is subjective or objective and if it contains irony marks [beta] at a global level, giving the user additional information about the reliability of the polarity obtained from the sentiment analysis.