What is Semantic Analysis? Definition, Examples, & Applications In 2023

As a systematic mapping, our study follows the principles of a systematic mapping/review. However, as our goal was to develop a general mapping of a broad field, our study differs from the procedure suggested by Kitchenham and Charters [3] in two ways. Firstly, Kitchenham and Charters [3] state that the systematic review should be performed by two or more researchers.

• Semiotics refers to what the word means and also the meaning it evokes or communicates.
• Furthermore, this survey highlights the advantages of semantic text classification algorithms over the traditional text classification algorithms.
• As we enter the era of ‘data explosion,’ it is vital for organizations to optimize this excess yet valuable data and derive valuable insights to drive their business goals.
• This results in state-of-the-art performance on multiple lexical entailment tasks.
• When we start to break our data down into the 3 components, we can actually choose the number of topics — we could choose to have 10,000 different topics, if we genuinely thought that was reasonable.

This paper aims to point some directions to the reader who is interested in semantics-concerned text mining researches. Many studies in the scientific literature (Aas and Eikvil, 1999, Aggarwal and Zhai, 2012, Berry, 2004, Hotho et al., 2005, Sebastiani, 2005) focus on traditional methods for text mining. Furthermore, there are also surveys that focus on particular type of classification algorithms such as kernel methods (Campbell, 2002, Jäkel et al., 2007).

Methods and algorithms

Thanks to tools like chatbots and dynamic FAQs, your customer service is supported in its day-to-day management of customer inquiries. The semantic analysis technology behind these solutions provides a better understanding of users and user needs. These solutions can provide instantaneous and relevant solutions, autonomously and 24/7. B2B and B2C companies are not the only ones to deploy systems of semantic analysis to optimize the customer experience. Google developed its own semantic tool to improve the understanding of user searchers. This technology is already in use and is analysing the emotion and meaning of exchanges between humans and machines.

The use of features based on WordNet has been applied with and without good results [55, 67–69]. Besides, WordNet can support the computation of semantic similarity [70, 71] and the evaluation of the discovered knowledge [72]. Grobelnik [14] also presents the levels of text representations, that differ from each other by the complexity of processing and expressiveness.

Techniques of Semantic Analysis

The second most used source is Wikipedia [73], which covers a wide range of subjects and has the advantage of presenting the same concept in different languages. Wikipedia concepts, as well as their links and categories, are also useful for enriching text representation [74–77] or classifying documents [78–80]. Bos [31] presents an extensive survey of computational semantics, a research area focused on computationally understanding human language in written or spoken form. He discusses how to represent semantics in order to capture the meaning of human language, how to construct these representations from natural language expressions, and how to draw inferences from the semantic representations.

Thus, there is a lack of studies dealing with texts written in other languages. When considering semantics-concerned text mining, we believe that this lack can be filled with the development of good knowledge bases and natural language processing methods specific for these languages. Besides, the analysis of the impact of languages in semantic-concerned text mining is also an interesting open research question.

Tasks involved in Semantic Analysis

The author also discusses the generation of background knowledge, which can support reasoning tasks. Bos [31] indicates machine learning, knowledge resources, and scaling inference as topics that can have a big impact on computational semantics in the future. Wimalasuriya and Dou [17], Bharathi and Venkatesan [18], and Reshadat and Feizi-Derakhshi [19] consider the use of external knowledge sources (e.g., ontology or thesaurus) in the text mining process, each one dealing with a specific task. Wimalasuriya text semantic analysis and Dou [17] present a detailed literature review of ontology-based information extraction. The authors define the recent information extraction subfield, named ontology-based information extraction (OBIE), identifying key characteristics of the OBIE systems that differentiate them from general information extraction systems. Bharathi and Venkatesan [18] present a brief description of several studies that use external knowledge sources as background knowledge for document clustering.

Here the generic term is known as hypernym and its instances are called hyponyms. Maps are essential to Uber’s cab services of destination search, routing, and prediction of the estimated arrival time (ETA). Along with services, it also improves the overall experience of the riders and drivers. Semantic analysis also takes into account signs and symbols (semiotics) and collocations (words that often go together). In Meaning Representation, we employ these basic units to represent textual information. Semantic Analysis is a topic of NLP which is explained on the GeeksforGeeks blog.

Although there is not a consensual definition established among the different research communities [1], text mining can be seen as a set of methods used to analyze unstructured data and discover patterns that were unknown beforehand [2]. Semantic analysis helps in processing customer queries and understanding their meaning, thereby allowing an organization to understand the customer’s inclination. Moreover, analyzing customer reviews, feedback, or satisfaction surveys helps understand the overall customer experience by factoring in language tone, emotions, and even sentiments. With the large amounts of text data that businesses process every day, many companies are sitting on large untapped goldmines in the form of unstructured text documents. Using text analysis to break these massive amounts of data into sentences, phrases, keywords and sentiment, can allow you to better understand trends and topics present across all your files – regardless of the source. Semantic analysis can also benefit SEO (search engine optimisation) by helping to decode the content of a users’ Google searches and to be able to offer optimised and correctly referenced content.

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