A Survey of Semantic Analysis Approaches SpringerLink

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” alt=”semantic analysis of text” width=”303px” />

Besides, linguistic resources as semantic networks or lexical databases, which are language-specific, can be used to enrich textual data. Thus, the low number of annotated data or linguistic resources can be a bottleneck when working with another semantic analysis of text language. There are important initiatives to the development of researches for other languages, as an example, we have the ACM Transactions on Asian and Low-Resource Language Information Processdeuce vaughn jersey fsu jersey brock purdy jersey penn state jersey justin jefferson lsu jersey tom brady michigan jersey fsu jersey deuce vaughn jersey tom brady michigan jersey custom ohio state jersey deuce vaughn jersey deuce vaughn jersey aiyuk jersey deuce vaughn jersey deuce vaughn jersey ing [50], an ACM journal specific for that subject.

The protocol is a documentation of the review process and must have all the information needed to perform the literature review in a systematic way. The analysis of selected studies, which is performed in the data extraction phase, will provide the answers to the research questions that motivated the literature review. Kitchenham and Charters [3] present a very useful guideline for planning and conducting systematic literature reviews.

Concepts

As systematic reviews follow a formal, well-defined, and documented protocol, they tend to be less biased and more reproducible than a regular literature review. In this step, raw text is transformed into some data representation format that can be used as input for the knowledge extraction algorithms. The activities performed in the pre-processing step are crucial for the success of the whole text mining process. The data representation must preserve the patterns hidden in the documents in a way that they can be discovered in the next step. In the pattern extraction step, the analyst applies a suitable algorithm to extract the hidden patterns.

Moreover, it is also helpful to customers as the technology enhances the overall customer experience at different levels. 1 A simple search for “systematic review” on the Scopus database in June 2016 returned, by subject area, 130,546 Health Sciences documents (125,254 of them for Medicine) and only 5,539 Physical Sciences (1328 of them for Computer Science). The coverage of Scopus publications are balanced between Health Sciences (32% of total Scopus publication) and Physical Sciences (29% of total Scopus publication). 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].

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The idea of entity extraction is to identify named entities in text, such as names of people, companies, places, etc. In other words, we can say that polysemy has the same spelling but different and related meanings. Semantic analysis aids in analyzing and understanding customer queries, helping to provide more accurate and efficient support. Semantic analysis employs various methods, but they all aim to comprehend the text’s meaning in a manner comparable to that of a human. This can entail figuring out the text’s primary ideas and themes and their connections. Continue reading this blog to learn more about semantic analysis and how it can work with examples.

In other words, it shows how to put together entities, concepts, relations, and predicates to describe a situation. Moreover, QuestionPro might connect with other specialized semantic analysis tools or NLP platforms, depending on its integrations or APIs. This integration could enhance the analysis by leveraging more advanced semantic processing capabilities from external tools. Moreover, while these are just a few areas where the analysis finds significant applications. Its potential reaches into numerous other domains where understanding language’s meaning and context is crucial.

For example, perception of the text as a bag of paragraphs can be accounted by exactly the same model that works with words and sentences. In that way, hierarchical semantic structure of information representation, typical to human cognition9,150, can be accessed. Impossibility of factorization (7) known as entanglement103 is a property of a compound state (4) in which subsystems have potential for coordinated resolution of uncertainties.

• Figure 10 presents types of user’s participation identified in the literature mapping studies.
• Hence, it is critical to identify which meaning suits the word depending on its usage.
• Recruiters and HR personnel can use natural language processing to sift through hundreds of resumes, picking out promising candidates based on keywords, education, skills and other criteria.
• These assistants are a form of conversational AI that can carry on more sophisticated discussions.

In order to fill this gap, we undertake a comprehensive discussion of semantic text classification vs. traditional text classification. Furthermore, this survey highlights the advantages of semantic text classification algorithms over the traditional text classification algorithms. Semantics is a branch of linguistics, which aims to investigate the meaning of language. Semantics deals with the meaning of sentences and words as fundamentals in the world.

How does semantic analysis work?

Nowadays, any person can create content in the web, either to share his/her opinion about some product or service or to report something that is taking place in his/her neighborhood. Companies, organizations, and researchers are aware of this fact, so they are increasingly interested in using this information in their favor. Some competitive advantages that business can gain from the analysis of social media texts are presented in [47–49]. The authors developed case studies demonstrating how text mining can be applied in social media intelligence. From our systematic mapping data, we found that Twitter is the most popular source of web texts and its posts are commonly used for sentiment analysis or event extraction. 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.

Moreover, with the ability to capture the context of user searches, the engine can provide accurate and relevant results. These chatbots act as semantic analysis tools that are enabled with keyword recognition and conversational capabilities. These tools help resolve customer problems in minimal time, thereby increasing customer satisfaction. Thus, as and when a new change is introduced on the Uber app, the semantic analysis algorithms start listening to social network feeds to understand whether users are happy about the update or if it needs further refinement.

This challenge is a frequent roadblock for artificial intelligence (AI) initiatives that tackle language-intensive processes. The goal of NER is to extract and label these named entities to better understand the structure and meaning of the text. As illustrated earlier, the word “ring” is ambiguous, as it can refer to both a piece of jewelry worn on the finger and the sound of a bell. To disambiguate the word and select the most appropriate meaning based on the given context, we used the NLTK libraries and the Lesk algorithm. Analyzing the provided sentence, the most suitable interpretation of “ring” is a piece of jewelry worn on the finger. Now, let’s examine the output of the aforementioned code to verify if it correctly identified the intended meaning.

In short, sentiment analysis can streamline and boost successful business strategies for enterprises. All in all, semantic analysis enables chatbots to focus on user needs and address their queries in lesser time and lower cost. In the following subsections, we describe our systematic mapping protocol and how this study was conducted. Analyzing the meaning of the client’s words is a golden lever, deploying operational improvements and bringing services to the clientele. 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.

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These solutions can provide instantaneous and relevant solutions, autonomously and 24/7. 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. The goal is to boost traffic, all while improving the relevance of results for the user. As such, semantic analysis helps position the content of a website based on a number of specific keywords (with expressions like “long tail” keywords) in order to multiply the available entry points to a certain page. The challenge of semantic analysis is understanding a message by interpreting its tone, meaning, emotions and sentiment. Today, this method reconciles humans and technology, proposing efficient solutions, notably when it comes to a brand’s customer service.