1,721,090 research outputs found
A fuzzy-oriented sentic analysis to capture the human emotion in Web-based content
No full textCapturing the sentiments and the emotional states enclosed in textual information is a critical task which embraces a wide range of web-oriented activities such as detecting the sentiments associated to the product reviews, developing marketing programs that would be attractive for users, enhancing customer service with respect to its expectation until to identifying new opportunities and financial market prediction, besides managing reputations. Opinions and the emotions that are embedded in them, play a key role in decision-making processes, with different effects depending on the negative or positive valence of the mood. When the choice depends on some important features (i.e., time, money, reliability/efficacy, etc.) and on other opinions (which come from previous experience), could be crucial to make the best decision.
Inferring opinions and emotions enclosed in the written language is a complex task which cannot rely on body languages (posture, gestures, vocal inflections), rather than discovering concepts with an affective valence. The role of opinions extracted by the social content is crucial to support consumers’ decision process; in addition, thanks opinions and emotions, it is possible to evidence improvements on existing decision supports and show how the opinion-mining techniques can be incorporated into these systems.
This paper presents a tentative contribution that addresses this issue: it introduces a framework for extracting the emotions and the sentiments expressed in the textual data. The sentiments are expressed by a positive or negative polarity, the emotions are based on the Minsky’s conception of emotions, that consists of four affective dimensions, each one with six levels of activations [1]. Sentiments and emotions are modeled as fuzzy sets; particularly, the intensity of the emotions has been tuned by fuzzy modifiers, which act on the linguistic patterns recognized in the sentences. The approach has been tested on some sets of documents categories, revealing interesting performance on the global framework processing
Semantic Web Content Analysis: A Study in Proximity-Based Collaborative Clustering
No full textThe 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,
the interoperability and the composition of several services. The
Semantic Web forms a new scenario, where advanced methods
and techniques are developed for the description, the retrieval and
filtering of Web-based content. In the light of existing challenges
and open issues concerning the actual cyberspace, this study
proposes an approach for binding the “semantic” facet with the
usual textual one, that together constitutes a typical web page,
or specifically, a semantic web document. Through the use of
unsupervised learning, we offer a new alternative of organizing
web documents which emphasizes a direct separation between the
syntactic and semantic facets of the web information. In this study,
we discuss a collaborative proximity-based fuzzy clustering and
show how this type of clustering is used to discover a structure
of web information by a prudent reliance on the structures in the
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
essential technique for the next generation of Web search engines
Similarity-based SLD resolution and its role for web knowledge discovery
No full textThis work presents the implementation of an extension of SLD resolution towards approximate reasoning and its implementation in an extended Prolog system. The proposed refutation procedure overcomes failures in the unification process by exploiting similarity relations defined between predicate and constant symbols. This enables to compute approximate solutions, with an associated approximation degree, when failures of the exact inference process occur. In this paper we outline the main ideas of this approach and we present an extended PROLOG interpreter, named SiLog, which implements this inference procedure. Then we point out on a web-based platform, usable for knowledge discovery, that exploits as inner feature the similarity-based SLD resolution
Mobile mail-agents through similarity-based reasoning.
No full textBots, or software agents are programs designed
to perform tasks autonomously. Mailbots attempt to provide useful functions about electronic mail (E-mail) service such as filtering information, gathering information,
and scheduling. With Internet use continuing to explode,
the information overload is growing so fast that the same
virtues that made E-mail so popular are now becoming a
negative technologic ‘‘boomerang’’ (see the volume of junk
or spam mail). Industrial as well as academic research has
faced this problem in terms of automated filtering
methods in order to distinguish legtimate E-mail from
spamming. Here we describe an alternative approach: our
mailbot is skilled to find ‘‘appropriate’’ destination of the
message triggering a spidering process on an Intranetbased network. The spidering performs a distributed,
mobile computation via pervasive agents: by applying a
similarity-based reasoning on designed users resources the
agents are able to deduct if the contacted user may be
interested or not in receiving the E-mail. The overall
architecture is implemented in Java using the basic issues
of Internet protocol
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