| Resumen |
Emulating the human brain is one of the core challenges of computational intelligence, which entails
many key problems of artificial intelligence, including understanding human language, reasoning, and emotions.
In this work, computational intelligence techniques are combined with common-sense computing and linguistics
to analyze sentiment data flows, i.e., to automatically decode how humans express emotions and opinions via
natural language. The increasing availability of social data is extremely beneficial for tasks such as branding, product positioning, corporate reputation management, and social media marketing. The elicitation of useful information from this huge amount of unstructured data, however, remains an open challenge. Although such data
are easily accessible to humans, they are not suitable for automatic processing: machines are still unable to effectively and dynamically interpret the meaning associated with natural language text in very large, heterogeneous, noisy, and ambiguous environments such as the Web. We present a novel methodology that goes beyond mere word-level analysis of text and enables a more efficient transformation of unstructured social data into structured information, readily interpretable by machines. In particular, we describe a novel paradigm for real-time concept-level sentiment analysis that blends computational intelligence, linguistics, and common-sense computing in order to improve the accuracy of computationally expensive t |
