Talking about SenticNet is talking about concept-level sentiment analysis, that is, performing tasks such as polarity detection and emotion recognition by leveraging on semantics and linguistics in stead of solely relying on word co-occurrence frequencies.
In this context, SenticNet can be one of the following things:
1) a concept-level knowledge base;
2) a multi-disciplinary framework;
3) a private company.
As a knowledge base, SenticNet provides a set of semantics, sentics, and polarity associated with 100,000 natural language concepts. In particular, semantics are concepts that are most semantically-related to the input concept (i.e., the five concepts that share more semantic features with the input concept), sentics are emotion categorization values expressed in terms of four affective dimensions (Pleasantness, Attention, Sensitivity, and Aptitude) and polarity is floating number between -1 and +1 (where -1 is extreme negativity and +1 is extreme positivity). The knowledge base is downloadable for free as a standalone XML file and its latest version (released every two years) is also accessible as an API.
As a framework, SenticNet consists of a set of tools and techniques for sentiment analysis combining commonsense reasoning, psychology, linguistics, and machine learning. In this context, SenticNet is more commonly referred to as sentic computing, a multi-disciplinary paradigm that goes beyond mere statistical approaches to sentiment analysis by focusing on a semantic-preserving representation of natural language concepts and on sentence structure.
As a company, finally, SenticNet puts together the latest findings in concept-level sentiment analysis to offer easy-to-use state-of-the-art tools for big social data analysis that enable the automation of tasks such as brand positioning, trend discovery, and social media marketing in different domains, languages, and modalities.