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

Finally, it analyzes the surrounding text and text structure to accurately determine the proper meaning of the words in context. Stavrianou et al. [15] also present the relation between ontologies and text mining. Ontologies can be used as background knowledge in a text mining process, and the text mining techniques can be used to generate and update ontologies. The mapping reported in this paper was conducted with the general goal of providing an overview of the researches developed by the text mining community and that are concerned about text semantics. This mapping is based on 1693 studies selected as described in the previous section. We can note that text semantics has been addressed more frequently in the last years, when a higher number of text mining studies showed some interest in text semantics.

In real application of the text mining process, the participation of domain experts can be crucial to its success. However, the participation of users (domain experts) is seldom explored in scientific papers. The difficulty inherent to the evaluation of a method based on user’s interaction is a probable reason for the lack of studies considering this approach.

Semantic Analysis Is Part of a Semantic System

The review reported in this paper is the result of a systematic mapping study, which is a particular type of systematic literature review [3, 4]. Systematic literature review is a formal literature review adopted to identify, evaluate, and synthesize evidences of empirical results in order to answer a research question. It is extensively applied in medicine, as part of the evidence-based medicine [5]. This type of literature review is not as disseminated in the computer science field as it is in the medicine and health care fields1, although computer science researches can also take advantage of this type of review. We can find important reports on the use of systematic reviews specially in the software engineering community [3, 4, 6, 7].

It’s an essential sub-task of Natural Language Processing (NLP) and the driving force behind machine learning tools like chatbots, search engines, and text analysis. Consider the task of text summarization which is used to create digestible chunks of information from large quantities of text. Text summarization extracts words, phrases, and sentences to form a text summary that can be more easily consumed. The accuracy of the summary depends on a machine’s ability to understand language data. Textual analysis in the social sciences sometimes takes a more quantitative approach, where the features of texts are measured numerically.

Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

When the field of interest is broad and the objective is to have an overview of what is being developed in the research field, it is recommended to apply a particular type of systematic review named systematic mapping study [3, 4]. Systematic mapping studies follow an well-defined protocol as in any systematic review. The main differences between a traditional systematic review and a systematic mapping are their breadth and depth. While a systematic review deeply analyzes a low number of primary studies, in a systematic mapping a wider number of studies are analyzed, but less detailed.

• This mapping is based on 1693 studies selected as described in the previous section.
• Semantic analysis helps in processing customer queries and understanding their meaning, thereby allowing an organization to understand the customer’s inclination.
• Most of the questions are related to text pre-processing and the authors present the impacts of performing or not some pre-processing activities, such as stopwords removal, stemming, word sense disambiguation, and tagging.

Besides the vector space model, there are text representations based on networks (or graphs), which can make use of some text semantic features. Network-based representations, such as bipartite networks and co-occurrence networks, can represent relationships between terms or between documents, which is not possible through the vector space model [147, 156–158]. In this study, we identified the languages that were mentioned in paper abstracts. We must note that English can be seen as a standard language in scientific publications; thus, papers whose results were tested only in English datasets may not mention the language, as examples, we can cite [51–56]. Besides, we can find some studies that do not use any linguistic resource and thus are language independent, as in [57–61].

Looking for the answer to this question, we conducted this systematic mapping based on 1693 studies, accepted among the 3984 studies identified in five digital libraries. In the previous subsections, we presented the mapping regarding to each secondary research question. In this subsection, we present a consolidation of our results and point some future trends of semantics-concerned text mining.

• Recent work in sociology of culture, science, and economic sociology has shown how computational text analysis can be used in theory building and testing.
• In this component, we combined the individual words to provide meaning in sentences.
• Nevertheless, it is also an interactive process, and there are some points where a user, normally a domain expert, can contribute to the process by providing his/her previous knowledge and interests.
• So, in this part of this series, we will start our discussion on Semantic analysis, which is a level of the NLP tasks, and see all the important terminologies or concepts in this analysis.

Semantic Analysis helps machines interpret the meaning of texts and extract useful information, thus providing invaluable data while reducing manual efforts. Insights derived from data also help teams detect areas of improvement and make better decisions. For example, you might decide to create a strong knowledge base by identifying the most common customer inquiries. You understand that a customer is frustrated because a customer service agent is taking too long to respond. Capturing the information is the easy part but understanding what is being said (and doing this at scale) is a whole different story. Leser and Hakenberg [25] presents a survey of biomedical named entity recognition.

Computer Speech & Language

On the other hand, clustering is the task of grouping examples (whose classes are unknown) based on their similarities. As these are basic text mining tasks, they are often the basis of other more specific text mining tasks, such as sentiment analysis and automatic ontology building. Therefore, it was expected that classification and clustering would be the most frequently applied tasks. When looking at the external knowledge sources used in semantics-concerned text mining studies (Fig. 7), WordNet is the most used source. This lexical resource is cited by 29.9% of the studies that uses information beyond the text data.

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