With the explosion of social media, automatic analysis of sentiment and emotion from user‐generated content has attracted the attention of many research areas and commercial‐marketing domains targeted at studying the social behavior of web users and their public attitudes toward brands, social events, and political actions. Capturing the emotions expressed in the written language could be crucial to support the decision‐making processes: the emotion resulting from a tweet or a review about an item could affect the way to advertise or to trade on the web and then to make predictions about future changes in popularity or market behavior. This paper presents an experience with the emotion‐based classification of textual data from a social network by using an extended version of the fuzzy C‐means algorithm called extension of fuzzy C‐means. The algorithm shows interesting results due to its intrinsic fuzzy nature that reflects the human feeling expressed in the text, often composed of a mix of blurred emotions, and at the same time, the benefits of the extended version yield better classification results.

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The semantic vision of the Web involves the processing of data by automated tools as well as by people, where the association of meaning with content, facilitates the search,\ud the interoperability and the composition of several services. The\ud Semantic Web forms a new scenario, where advanced methods\ud and techniques are developed for the description, the retrieval and\ud filtering of Web-based content. In the light of existing challenges\ud and open issues concerning the actual cyberspace, this study\ud proposes an approach for binding the “semantic” facet with the\ud usual textual one, that together constitutes a typical web page,\ud or specifically, a semantic web document. Through the use of\ud unsupervised learning, we offer a new alternative of organizing\ud web documents which emphasizes a direct separation between the\ud syntactic and semantic facets of the web information. In this study,\ud we discuss a collaborative proximity-based fuzzy clustering and\ud show how this type of clustering is used to discover a structure\ud of web information by a prudent reliance on the structures in the\ud spaces of semantics and data. The method focuses on the reconciliation between the two separated facets of web information and a combination of results leading to a comprehensive data organization. The information arranged in this manner can provide an integral description of web resources, becoming in this manner an\ud essential technique for the next generation of Web search engines

Semantic annotation is at the core of Semantic Web technology: it bridges the gap between legacy non-semantic web resource descriptions and their elicited, formally specified conceptualization, converting syntactic structures into knowledge structures, i.e., ontologies. Most existing approaches and tools are designed to deal with manual or semi-/automatic semantic annotation that exploits available ontologies through the pattern-based discovery of concepts. This work aims to generate the automatic semantic annotation of web resources, without any prefixed ontological support. The novelty of our approach is that, starting from web resources, content with a high-level of abstraction is obtained: concepts, connections between concepts, and instance-population are identified and arranged into an ex-novo ontology. The framework is designed to process resources from different sources (textual information, images, etc.) and generate an ontology-based annotation. A data-driven analysis reveals the data and their intrinsic relationships (in the form of triples) extracted from the resource content. On the basis of the discovered semantics, corresponding concepts and properties are modeled, allowing an ad hoc ontology to be built through an OWL-based coding annotation. The benefit of this approach is the generation of knowledge structured in a quite automatic way (i.e., the human support is restricted to the configuration of some parameters). The approach exploits a fuzzy extension of the mathematical modeling of Formal Concept Analysis and Relational Concept Analysis to generate the ontological structure of data resources