Semantic Features Analysis Definition, Examples, Applications

Semantic, Pragmatic and Discourse Analysis SpringerLink

The authors state that automatic ontology building from texts is the way to the timely production of ontologies for current applications and that many questions are still open in this field. The authors divide the ontology learning problem into seven tasks and discuss their developments. They state that ontology population task seems to be easier than learning ontology schema tasks.

This application domain is followed by the Web domain, what can be explained by the constant growth, in both quantity and coverage, of Web content. As previously stated, the objective of this systematic mapping is to provide a general overview of semantics-concerned text mining studies. The papers considered in this systematic mapping study, as well as the mapping results, are limited by the applied search expression and the research questions. Therefore, the reader can miss in this systematic mapping report some previously known studies.

Method applied for systematic mapping

But before deep dive into the concept and approaches related to meaning representation, firstly we have to understand the building blocks of the semantic system. It’s used extensively in NLP tasks like sentiment analysis, document summarization, machine translation, and question answering, thus showcasing its versatility and fundamental role in processing language. Semantic analysis forms the backbone of many NLP tasks, enabling machines to understand and process language more effectively, leading to improved machine translation, sentiment analysis, etc. 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.

Wikipedia concepts, as well as their links and categories, are also useful for enriching text representation [74–77] or classifying documents [78–80]. This paper reports a systematic mapping study conducted to get a general overview of how text semantics is being treated in text mining studies. It fills a literature review gap in this broad research field through a well-defined review process. 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.

Syntactic and Semantic Analysis

Lexical analysis is based on smaller tokens but on the contrary, the semantic analysis focuses on larger chunks. This is often accomplished by locating and extracting the key ideas and connections found in the text utilizing algorithms and AI approaches. Semantic analysis allows for a deeper understanding of user preferences, enabling personalized recommendations in e-commerce, content semantic analysis of text curation, and more. Thus, the ability of a machine to overcome the ambiguity involved in identifying the meaning of a word based on its usage and context is called Word Sense Disambiguation. With sentiment analysis, companies can gauge user intent, evaluate their experience, and accordingly plan on how to address their problems and execute advertising or marketing campaigns.

As ensuring relevance requires prognosis of the user’s judgment, effective algorithms are bound, in some form, to simulate human-kind linguistic practice. This is an unsolved challenge, complexity of which was recognized long before computer age1,2,3,4. Now, with reading and writing texts turned into a massive and influencing part of creative human behavior, the problem is brought to the forefront of information technologies. Harnessing of human language skills is expected to bring machine intelligence to a new level of capability5,6,7. In AI and machine learning, semantic analysis helps in feature extraction, sentiment analysis, and understanding relationships in data, which enhances the performance of models. Semantic analysis is defined as a process of understanding natural language (text) by extracting insightful information such as context, emotions, and sentiments from unstructured data.

The question of quantumness

Positive results obtained on a limited corpus of documents indicate potential of the developed theory for semantic analysis of natural language. Several companies are using the sentiment analysis functionality to understand the voice of their customers, extract sentiments and emotions from text, and, in turn, derive actionable data from them. It helps capture the tone of customers when they post reviews and opinions on social media posts or company websites. Cdiscount, an online retailer of goods and services, uses semantic analysis to analyze and understand online customer reviews.

• Grobelnik [14] also presents the levels of text representations, that differ from each other by the complexity of processing and expressiveness.
• Moreover, it is also helpful to customers as the technology enhances the overall customer experience at different levels.
• Only the broader context of document is used with some type of term frequency information in the vector space.
• This is how to use the tf-idf to indicate the importance of words or terms inside a collection of documents.

It recreates a crucial role in enhancing the understanding of data for machine learning models, thereby making them capable of reasoning and understanding context more effectively. Semantic analysis aids search engines in comprehending user queries more effectively, consequently retrieving more relevant results by considering the meaning of words, phrases, and context. Search engines use semantic analysis to understand better and analyze user intent as they search for information on the web.