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

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. Semantic analysis analyzes the grammatical format of sentences, including the arrangement of words, phrases, and clauses, to determine relationships between independent terms in a specific context. It is also a key component of several machine learning tools available today, such as search engines, chatbots, and text analysis software. Semantic Analyzer is an open-source tool that combines interactive visualisations and machine learning to support users in fast prototyping the semantic analysis of a large collection of textual documents. The principal innovation of the Semantic Analyzer lies in the combination of interactive visualisations, visual programming approach, and advanced tools for text modelling. The target audience of the tool are data owners and problem domain experts from public administration.

We believe that this tool has the potential to be used for other organisations from the public and private sector and for other interested parties (e. g. academia, students, or other citizens) in the future. The semantic analyser scans the texts in a collection and extracts characteristic concepts from them. Depending on which concepts appear in several texts at the same time, it reveals the relatedness between them and, according to this criterion, determines groups and classifies the texts among them.

Systematic mapping summary and future trends

As we look ahead, it’s evident that the confluence of human language and technology will only grow stronger, creating possibilities that we can only begin to imagine. Text classification and text clustering, as basic text mining tasks, are frequently applied in semantics-concerned text mining researches. Among other more specific tasks, sentiment analysis is a recent research field that is almost as applied as information retrieval and information extraction, which are more consolidated research areas. SentiWordNet, a lexical resource for sentiment analysis and opinion mining, is already among the most used external knowledge sources.

This step is termed ‘lexical semantics‘ and refers to fetching the dictionary definition for the words in the text. Each element is designated a grammatical role, and the whole structure is processed to cut down on any confusion caused by ambiguous words having multiple meanings. 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.

Text mining techniques have become essential for supporting knowledge discovery as the volume and variety of digital text documents have increased, either in social networks and the Web or inside organizations. 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]. Uber uses semantic analysis to analyze users’ satisfaction or dissatisfaction levels via social listening. This implies that whenever Uber releases an update or introduces new features via a new app version, the mobility service provider keeps track of social networks to understand user reviews and feelings on the latest app release. In semantic analysis, word sense disambiguation refers to an automated process of determining the sense or meaning of the word in a given context. As natural language consists of words with several meanings (polysemic), the objective here is to recognize the correct meaning based on its use.

This definition of amplitudes is by no means the only possible; it is chosen due to its sufficiency for the proof-of-principle demonstration pursued in this paper. 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. The world became more eco-conscious, EcoGuard developed a tool that uses semantic analysis to sift through global news articles, blogs, and reports to gauge the public sentiment towards various environmental issues. This AI-driven tool not only identifies factual data, like t he number of forest fires or oceanic pollution levels but also understands the public’s emotional response to these events. By correlating data and sentiments, EcoGuard provides actionable and valuable insights to NGOs, governments, and corporations to drive their environmental initiatives in alignment with public concerns and sentiments.

Text mining initiatives can get some advantage by using external sources of knowledge. Thesauruses, taxonomies, ontologies, and semantic networks are knowledge sources that are commonly used by the text mining community. Semantic networks is a network whose nodes are concepts that are linked by semantic relations. The most popular example is the WordNet [63], an electronic lexical database developed at the Princeton University. Depending on its usage, WordNet can also be seen as a thesaurus or a dictionary [64].

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Cognitive states formed in the process of perception of text are fully compatible with quantum theoretic analysis methods. In this way, concurrence measure of quantum entanglement is imported from quantum theory to the cognitive domain for free. The resulting model quantifies subjective familiarity between cognitive entities that is an essential in knowledge systems36,124. In texts, it allows to extract and quantify meaning relations between concepts, requested for semantic analysis of natural language data125,126,127.

This allows to build explicit and compact cognitive-semantic representations of user’s interest, documents, and queries, subject to simple familiarity measures generalizing usual vector-to-vector cosine distance. The result is more precise estimation of subjective relevance judgments leading to better composition of search result pages40,41,42,43. Quantitative models of natural language are applied in information retrieval industry as methods for meaning-based processing of textual data.

• Among the three words, “peanut”, “jumbo” and “error”, tf-idf gives the highest weight to “jumbo”.
• Cdiscount, an online retailer of goods and services, uses semantic analysis to analyze and understand online customer reviews.
• Use of different Pauli operators in (8) may account for distinction between classical and quantum-like aspects of semantics102.
• Wikipedia concepts, as well as their links and categories, are also useful for enriching text representation [74–77] or classifying documents [78–80].
• Despite the fact that the user would have an important role in a real application of text mining methods, there is not much investment on user’s interaction in text mining research studies.
• 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.

Upgrading quantum decision model from descriptive to predictive status is possible by supplying it with quantum phase regularities encoding semantic stability of cognitive patterns144,145. Concurrence value (10) defines maximal violation of Bell’s inequality also used to detect entanglement of two-qubit state (4) in quantum physics and informatics87,111. This relates the model of perception semantics developed in this paper with Bell-based methods for quantification of quantum-like contextuality and semantics in cognition and behavior106,107,112,113. Concurrence entanglement measure of the two-qubit cognitive state can be compared with quantification of semantic connection by Bell-like inequality introduced in114. Use of different Pauli operators in (8) may account for distinction between classical and quantum-like aspects of semantics102.

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Possible approach to this problem is suggested by neurophysiological parallel of quantum cognitive modeling developed in “Results” section. According to this correspondence, quantum phases are phases of neural oscillation modes65,140,141,142, encoding cognitive distinctions represented by quantum qubit states as shown in Fig. With the help of semantic analysis, machine learning tools can recognize a ticket either as a “Payment issue” or a“Shipping problem”.

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. It’s not just about understanding text; it’s about inferring intent, unraveling emotions, and enabling machines to interpret human communication with remarkable accuracy and depth. From optimizing data-driven strategies to refining automated processes, semantic analysis serves as the backbone, transforming how machines comprehend language and enhancing human-technology interactions.

• It then identifies the textual elements and assigns them to their logical and grammatical roles.
• The second most frequent identified application domain is the mining of web texts, comprising web pages, blogs, reviews, web forums, social medias, and email filtering [41–46].
• This step is termed ‘lexical semantics‘ and refers to fetching the dictionary definition for the words in the text.
• The selection and the information extraction phases were performed with support of the Start tool [13].

Its results were based on 1693 studies, selected among 3984 studies identified in five digital libraries. The produced mapping gives a general summary of the subject, points some areas that lacks the development of primary or secondary studies, and can be a guide for researchers working with semantics-concerned text mining. It demonstrates that, although several studies have been developed, the processing of semantic aspects in text mining remains an open research problem. Beyond latent semantics, the use of concepts or topics found in the documents is also a common approach.

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. Sentiment analysis, a subset of semantic analysis, dives deep into textual data to gauge emotions and sentiments.

Secondly, systematic reviews usually are done based on primary studies only, nevertheless we have also accepted secondary studies (reviews or surveys) as we want an overview of all publications related to the theme. As text semantics has an important role in text meaning, the term semantics has been seen in a vast sort of text mining studies. However, there is a lack of studies that integrate the different research branches and summarize the developed works. This paper reports a systematic mapping about semantics-concerned text mining studies.

In the pattern extraction step, user’s participation can be required when applying a semi-supervised approach. In the post-processing step, the user can evaluate the results according to the expected knowledge usage. Traditionally, text mining techniques are based on both a bag-of-words representation and application of data mining techniques. In order to get a more complete analysis of text collections and get better text mining results, several researchers directed their attention to text semantics. Cdiscount, an online retailer of goods and services, uses semantic analysis to analyze and understand online customer reviews. When a user purchases an item on the ecommerce site, they can potentially give post-purchase feedback for their activity.

Neural basis of quantum cognitive modeling

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. The semantic analysis of text 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.

The authors compare 12 semantic tagging tools and present some characteristics that should be considered when choosing such type of tools. 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.

We found considerable differences in numbers of studies among different languages, since 71.4% of the identified studies deal with English and Chinese. 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. A comparison among semantic aspects of different languages and their impact on the results of text mining techniques would also be interesting. The results of the systematic mapping study is presented in the following subsections.

It understands the text within each ticket, filters it based on the context, and directs the tickets to the right person or department (IT help desk, legal or sales department, etc.). I need to process sentences, input by users and find if they are semantically close to words in the corpus that I have. Corresponding probabilistic regularity is represented by potentiality state \(\left| \Psi \right\rangle\) as indicated in the Fig. Observable judgment or decision making records transition of a cognitive-behavioral system from state \(\left| \Psi \right\rangle\) to a new state corresponding to the option actualized. Whether it is Siri, Alexa, or Google, they can all understand human language (mostly).

You can foun additiona information about ai customer service and artificial intelligence and NLP. Earlier search algorithms focused on keyword matching, but with semantic search, the emphasis is on understanding the intent behind the search query. If someone searches for “Apple not turning on,” the search engine recognizes that the user might be referring to an Apple product (like an iPhone or MacBook) that won’t power on, rather than the fruit. Semantic analysis stands as the cornerstone in navigating the complexities of unstructured data, revolutionizing how computer science approaches language comprehension. Its prowess in both lexical semantics and syntactic analysis enables the extraction of invaluable insights from diverse sources.

Studying the combination of Individual Words

Semantic analysis tech is highly beneficial for the customer service department of any company. Moreover, it is also helpful to customers as the technology enhances the overall customer experience at different levels. However, machines first need to be trained to make sense of human language and understand the context in which words are used; otherwise, they might misinterpret the word “joke” as positive. Now, we have a brief idea of meaning representation that shows how to put together the building blocks of semantic systems. In other words, it shows how to put together entities, concepts, relations, and predicates to describe a situation.

With the help of meaning representation, we can represent unambiguously, canonical forms at the lexical level. Usually, relationships involve two or more entities such as names of people, places, company names, etc. In this component, we combined the individual words to provide meaning in sentences.

Relative to the dichotomic alternative 0/1, potential outcomes of the experiment are encoded by superposition vector state \(\left| \Psi \right\rangle\) (1). If the experiment is performed, the system transfers to one of the superposed potential outcomes according to probabilities \(p_i\). Conversational chatbots have come a long way from rule-based systems to intelligent agents that can engage users in almost human-like conversations. The application of semantic analysis in chatbots allows them to understand the intent and context behind user queries, ensuring more accurate and relevant responses. For instance, if a user says, “I want to book a flight to Paris next Monday,” the chatbot understands not just the keywords but the underlying intent to make a booking, the destination being Paris, and the desired date. Semantic analysis significantly improves language understanding, enabling machines to process, analyze, and generate text with greater accuracy and context sensitivity.

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. Besides, Semantics Analysis is also widely employed to facilitate the processes of automated answering systems such as chatbots – that answer user queries without any human interventions. 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. Hence, under Compositional Semantics Analysis, we try to understand how combinations of individual words form the meaning of the text.

The distribution of text mining tasks identified in this literature mapping is presented in Fig. Classification corresponds to the task of finding a model from examples with known classes (labeled instances) in order to predict the classes of new examples. On the other hand, clustering is the task of grouping examples (whose classes are unknown) based on their similarities.

Moreover, granular insights derived from the text allow teams to identify the areas with loopholes and work on their improvement on priority. By using semantic analysis tools, concerned business stakeholders can improve decision-making and customer experience. Semantic analysis techniques and tools allow automated text classification or tickets, freeing the concerned staff from mundane and repetitive tasks. In the larger context, this enables agents to focus on the prioritization of urgent matters and deal with them on an immediate basis.

Using subjective relevance judgment as observable for semantic connectivity can be seen as inverse of the basic objective of information retrieval science aiming to rank text documents according to the user’s needs. Post-factum fitting of phase data presented above is in line with the basic practice of quantum cognitive modeling14,15. In the present case, it constitutes finding of what the perception state should be in order to agree with the expert’s document ranking in the best possible way.

However, many organizations struggle to capitalize on it because of their inability to analyze unstructured data. This challenge is a frequent roadblock for artificial intelligence (AI) initiatives that tackle language-intensive processes. In Natural Language, the meaning of a word may vary as per its usage in sentences and the context of the text. Word Sense Disambiguation involves interpreting the meaning of a word based upon the context of its occurrence in a text. Leser and Hakenberg [25] presents a survey of biomedical named entity recognition.

Cognitive and physiological terminologies reflect quantum-theoretic concepts (bold) in parallel way. In quantum approach, a cognitive-behavioral system is considered as a black box in relation to a potential alternative 0/1. Department of the black box responsible for the resolution of this alternative is observable, delineated from the context analogous to the Heienberg’s cut between the system and the apparatus in quantum physics.

This specifies level of semantics that can be detected as entanglement between corresponding cognitive representations. In short, semantic fields of words are represented by superposition potentiality states, actualizing into concrete meanings during interaction with particular contexts. Creative aspect of this subjectively-contextual process is a central feature of quantum-type phenomena, first observed in microscopic physical processes37,38. We also found some studies that use SentiWordNet [92], which is a lexical resource for sentiment analysis and opinion mining [93, 94].

Therefore, in semantic analysis with machine learning, computers use Word Sense Disambiguation to determine which meaning is correct in the given context. Moreover, QuestionPro typically provides visualization tools and reporting features to present survey data, including textual responses. These visualizations help identify trends or patterns within the unstructured text data, supporting the interpretation of semantic aspects to some extent.

Pairing QuestionPro’s survey features with specialized semantic analysis tools or NLP platforms allows for a deeper understanding of survey text data, yielding profound insights for improved decision-making. QuestionPro often includes text analytics features that perform sentiment analysis on open-ended survey responses. While not a full-fledged semantic analysis tool, it can help understand the general sentiment (positive, negative, neutral) expressed within the text. 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.

Less than 1% of the studies that were accepted in the first mapping cycle presented information about requiring some sort of user’s interaction in their abstract. To better analyze this question, in the mapping update performed in 2016, the full text of the studies were also considered. Figure 10 presents types of user’s participation identified in the literature mapping studies. The most common user’s interactions are the revision or refinement of text mining results [159–161] and the development of a standard reference, also called as gold standard or ground truth, which is used to evaluate text mining results [162–165].

The authors present the difficulties of both identifying entities (like genes, proteins, and diseases) and evaluating named entity recognition systems. They describe some annotated corpora and named entity recognition tools and state that the lack of corpora is an important bottleneck in the field. Some studies accepted in this systematic mapping are cited along the presentation of our mapping.

Schiessl and Bräscher [20] and Cimiano et al. [21] review the automatic construction of ontologies. Schiessl and Bräscher [20], the only identified review written in Portuguese, formally define the term ontology and discuss the automatic building of ontologies from texts. 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.