Automatic discovery of similarity relationships through Web mining

This study investigates the coverage of solid waste management on 1,142 websites maintained by companies, news media, and non-governmental organizations to validate an automated approach to content and language analysis. First, a frequency analysis of waste management terms is used to shed light on the breadth and depth of their environmental discourses, revealing that corporate and media attention to waste management is small compared to that of non-governmental organizations. Second, an investigation of their attitudes toward waste management suggests that companies avoid negative information in environmental communication, unlike news media or nongovernmental organizations. Ultimately, an automated tool for ontology building is employed to gain insights into companies' shared understanding of waste management. The ontology obtained indicates that companies conceptualize waste management as a business processes rather than framing it from an ecological perspective, which is in line with findings form previous research.

show abstract

“…• Co-occurrence analysis assumes that two semantically related terms regularly co-occur in the same documents (Roussinov and Zhao, 2003).…”

Section: Waste Management Ontologymentioning

confidence: 99%

“…The terms included in the seed ontology are then fed into the Lexical Analyzer. Cooccurrence analysis at both the sentence and the document level limits the influence of popular terms not related to the domain (Roussinov and Zhao, 2003). Terms are selected according to a threshold value on the co-occurrence significance.…”

Section: Waste Management Ontologymentioning

confidence: 99%

Web content mining for comparing corporate and third‐party online reporting: a case study on solid waste management

2006

View full text Add to dashboard Cite

show abstract

“…The need for identifying opinions has motivated a number of automated methods for detecting opinions or other subjective text passages [41][42][43][44][45][46][47] and assigning them to subcategories such as positive and negative opinions [48][49][50][51]. A variety of machine learning techniques have been employed for this purpose, generally based on lexical cues associated with opinions.…”

Section: Related Workmentioning

confidence: 99%

Using Bio-inspired intelligence for Web opinion Mining

Stylios¹,

Katsis²,

Christodoulakis³

2014

IJCA

View full text Add to dashboard Cite

This work proposes a bio-inspired based methodology in order to extract and evaluate user's web texts / posts. To validate the methodology, a dataset is constructed using real data arising from Greek fora. The obtained results are compared with a commonly used machine learning technique (decision trees-C4.5 algorithm). The bio-inspired algorithm (namely the hybrid PSO/ACO2 algorithm) achieved average classification accuracy 90.59% in a 10 fold cross validation experiment, outperforming the C4.5 algorithm (83.66%). The proposed methodology could be easily integrated with a decision support system providing services in the fields of ecommerce or e-government in order to help merchants acquire customer satisfaction or public administrators capture common understanding.

show abstract

“…Co-occurrence analysis at both the sentence and the document level limits the influence of popular terms that are not related to the domain (Roussinov & Zhao, 2003). Terms are selected according to a threshold value on the cooccurrence significance.…”

mentioning

confidence: 99%

Tracking and modelling information diffusion across interactive online media

Scharl

Weichselbraun

Liu

2007

IJMSO

View full text Add to dashboard Cite

Information spreads rapidly across Web sites, Web logs and online forums. This paper describes the research framework of the IDIOM Project (Information Diffusion across Interactive Online Media), 1 which analyzes this process by identifying redundant content elements, mapping them to an ontological knowledge structure, and tracking their temporal and geographic distribution. Linguists define "idiom" as an expression whose meaning is different from the literal meanings of its component words. Similarly, the study of information diffusion promises insights that cannot be inferred from individual network elements. This paper presents underlying technology, initial results, and the future roadmap of investigating information diffusion based on ontological knowledge structures. Similar projects often focus on particular media, or neglect important aspects of the human language. This paper addresses these gaps to reveal fundamental mechanisms of information diffusion across media with distinct interactive characteristics.

show abstract

Automatic discovery of similarity relationships through Web mining

Cited by 83 publications

References 19 publications

Web content mining for comparing corporate and third‐party online reporting: a case study on solid waste management

Web content mining for comparing corporate and third‐party online reporting: a case study on solid waste management

Using Bio-inspired intelligence for Web opinion Mining

Tracking and modelling information diffusion across interactive online media

Contact Info

Product

Resources

About