The use of emerging digital information has become significant and exponential, as well as the boom of social media (forms, blogs, and social networks). Sentiment analysis concerns the statistical analysis of the views expressed in written texts. In appropriate evaluations of the emotional context, semantics plays an important role. The analysis is generally done from two viewpoints: how semantics are coded in sentimental instruments, such as lexicon, corporate, and ontological, and how automated systems determine feelings on social data. Two approaches to evaluate sentiments are commonly adopted (i.e., approaches focused on machine learning algorithms and semantic approaches). The precise testing in this area was increased by the already advanced semantic technology. This chapter focuses on semantic guidance-based sentiment analysis approaches. The Twitter/Facebook data will provide a semantically enhanced technique for annotation of sentiment polarity.
TopOntology And The Semantic Web
Today the Internet has become a critical human need. People depend heavily on the Internet for their day-to-day tasks. World Wide Web (WWW) has rapidly become a massive database with some information on all of the interesting things. Most of the web content is primarily designed for human read, computers can only decode layout web pages (Kaur & Agrawal, 2017). Machines generally lack the automated processing of data collected from any website without any knowledge of their semantics.
This has become a concern because users spend a great deal of time comparing multiple websites. Semantic Web provides a solution to this problem. Semantic web is defined as a collection of technologies that enable computers to understand the meaning of metadata based information, i.e., information about the information content. Web Semantic can be applied to integrate information from heterogeneous sources and improve the search process for improved and consistent information (Jalota & Agrawal, 2019). The Semantic technologies allow the ontology to refer to a metadata.
Ontology is a description of a domain knowledge that includes various terminologies of a given domain along with the relationship between existing terms.
Ontology is designed to act as metadata. Ontologies can help to create conceptual search and navigation of semantics for integration of semantically in-order feature. The language structures used to constructs ontologies include: XML, XML Schema, RDF, OWL, and RDF Scheme.
OWL has benefits over other structure languages in that OWL has more facilities to express meaning and semantic than XML and RDF / s. Ontologies built using RDF, OWL etc. are linked in a structured way to express semantic content explicitly and organize semantic boundaries for extracting concrete information (Kalra & Agrawal, 2019).