Semantic Analyser Smart Text Search Engine Observatory of Public Sector Innovation

Firstly, Kitchenham and Charters [3] state that the systematic review should be performed by two or more researchers. Although our mapping study was planned by two researchers, the study selection and the information extraction phases were conducted by only one due to the resource constraints. In this process, the other researchers reviewed the execution of each systematic mapping phase and their results.

It is normally based on external knowledge sources and can also be based on machine learning methods [36, 130–133]. Figure 5 presents the domains where text semantics is most present in text mining applications. Health care and life sciences is the domain that stands out when talking about text semantics in text mining applications. This fact is not unexpected, since life sciences have a long time concern about standardization of vocabularies and taxonomies.

Besides the top 2 application domains, other domains that show up in our mapping refers to the mining of specific types of texts. We found research studies in mining news, scientific papers corpora, patents, and texts with economic and financial content. Specifically for the task of irony detection, Wallace [23] presents both philosophical formalisms and machine learning approaches. The author argues that a model of the speaker is necessary to improve current machine learning methods and enable their application in a general problem, independently of domain. He discusses the gaps of current methods and proposes a pragmatic context model for irony detection.

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. 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.

This ends our Part-9 of the Blog Series on Natural Language Processing!

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. 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. A probable reason is the difficulty inherent to an evaluation based on the user’s needs.

By understanding the underlying sentiments and specific issues, hospitals and clinics can tailor their services more effectively to patient needs. Machine Learning has not only enhanced the accuracy of semantic analysis but has also paved the way for scalable, real-time analysis of vast textual datasets. As the field of ML continues to evolve, it’s anticipated that machine learning tools and its integration with semantic analysis will yield even more refined and accurate insights into human language. Semantic analysis, a natural language processing method, entails examining the meaning of words and phrases to comprehend the intended purpose of a sentence or paragraph. Additionally, it delves into the contextual understanding and relationships between linguistic elements, enabling a deeper comprehension of textual content.

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. The lower number of studies in the year 2016 can be assigned to the fact that the last searches were conducted in February 2016.

We start our report presenting, in the “Surveys” section, a discussion about the eighteen secondary studies (surveys and reviews) that were identified in the systematic mapping. In the “Systematic mapping summary and future trends” section, we present a consolidation of our results and point some gaps of both primary and secondary studies. Semantic analysis, often referred to as meaning analysis, is a process used in linguistics, computer science, and data analytics to derive and understand the meaning of a given text or set of texts. In computer science, it’s extensively used in compiler design, where it ensures that the code written follows the correct syntax and semantics of the programming language. In the context of natural language processing and big data analytics, it delves into understanding the contextual meaning of individual words used, sentences, and even entire documents. By breaking down the linguistic constructs and relationships, semantic analysis helps machines to grasp the underlying significance, themes, and emotions carried by the text.

The “Method applied for systematic mapping” section presents an overview of systematic mapping method, since this is the type of literature review selected to develop this study and it is not widespread in the text mining community. In this section, we also present the protocol applied to conduct the systematic mapping study, including the research questions that guided this study and how it was conducted. The results of the systematic mapping, as well as identified future trends, are presented in the “Results and discussion” section. Google incorporated ‘semantic analysis’ into its framework by developing its tool to understand and improve user searches. The Hummingbird algorithm was formed in 2013 and helps analyze user intentions as and when they use the google search engine. As a result of Hummingbird, results are shortlisted based on the ‘semantic’ relevance of the keywords.

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.

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.

As such, Cdiscount was able to implement actions aiming to reinforce the conditions around product returns and deliveries (two criteria mentioned often in customer feedback). For example, the top 5 most useful feature selected by Chi-square test are “not”, “disappointed”, “very disappointed”, “not buy” and “worst”. The next most useful feature selected by Chi-square test is “great”, I assume it is from mostly the positive reviews.

What Semantic Analysis Means to Natural Language Processing

Consequently, in order to improve text mining results, many text mining researches claim that their solutions treat or consider text semantics in some way. However, text mining is a wide research field and there is a lack of secondary studies that summarize and integrate the different approaches. 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.

Full employment of these notions in methods of machine text analysis is expected to start new generation of meaning-based information science44. In AI and machine learning, semantic analysis helps in feature extraction, sentiment analysis, and understanding relationships in data, which enhances the performance of models. Text semantics is closely related to ontologies and other similar types of knowledge representation. We also know that health care and life sciences is traditionally concerned about standardization of their concepts and concepts relationships. You can foun additiona information about ai customer service and artificial intelligence and NLP. Thus, as we already expected, health care and life sciences was the most cited application domain among the literature accepted studies.

• With the help of semantic analysis, machine learning tools can recognize a ticket either as a “Payment issue” or a“Shipping problem”.
• Moreover, granular insights derived from the text allow teams to identify the areas with loopholes and work on their improvement on priority.
• Tools such as the Semantic Analyzer support the development of the data economy and digitisation more broadly and aim to democratise artificial intelligence.
• The authors present the difficulties of both identifying entities (like genes, proteins, and diseases) and evaluating named entity recognition systems.

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]. Other sparse initiatives can also be found in other computer science areas, as cloud-based environments [8], image pattern recognition [9], biometric authentication [10], recommender systems [11], and opinion mining [12]. 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. Quantum models, essentially, extend a standard vector representation of language semantics to a broader class of objects used by quantum theory to represent states of physical systems39.

Semantic Analysis Is Part of a Semantic System

Semantic analysis is a crucial component of natural language processing (NLP) that concentrates on understanding the meaning, interpretation, and relationships between words, phrases, and sentences in a given context. It goes beyond merely analyzing a sentence’s syntax (structure and grammar) and delves into the intended meaning. In this model, each document is represented by a vector whose dimensions correspond to features found in the corpus. Despite the good results achieved with a bag-of-words, this representation, based on independent words, cannot express word relationships, text syntax, or semantics.

It also shortens response time considerably, which keeps customers satisfied and happy. In semantic analysis with machine learning, computers use word sense disambiguation to determine which meaning is correct in the given context. Since 2019, Cdiscount has been using a semantic analysis solution to process all of its customer reviews online. This kind of system can detect priority axes of improvement to put in place, based on post-purchase feedback. The company can therefore analyze the satisfaction and dissatisfaction of different consumers through the semantic analysis of its reviews. Complex nature of these phenomena makes them problematic to account with classical reductionist approach.

Thus, the search terms of a systematic mapping are broader and the results are usually presented through graphs. A systematic review is performed in order to answer a research question and must follow a defined protocol. The protocol is developed when planning the systematic review, and it is mainly composed by the research questions, the strategies and criteria for searching for primary studies, study selection, and data extraction. 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.

Therefore, it is not a proper representation for all possible text mining applications. Grobelnik [14] also presents the levels of text representations, that differ from each other by the complexity of processing and expressiveness. The most simple level is the lexical level, which includes the common bag-of-words and n-grams representations. The next level is the syntactic level, that includes representations based on word co-location or part-of-speech tags. The most complete representation level is the semantic level and includes the representations based on word relationships, as the ontologies.

The analysis can segregate tickets based on their content, such as map data-related issues, and deliver them to the respective teams to handle. The platform allows Uber to streamline and optimize the map data triggering the ticket. Powerful semantic-enhanced machine learning tools will deliver valuable insights that drive better decision-making and improve customer experience. 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. Understanding of the phase parameters is a hard question in quantum cognitive and behavioral modeling.

Semantic analysis refers to a process of understanding natural language (text) by extracting insightful information such as context, emotions, and sentiments from unstructured data. It gives computers and systems the ability to understand, interpret, and derive meanings from sentences, paragraphs, reports, registers, files, or any document of a similar kind. The development of tools is necessary to further develop analytical techniques in the field of text analysis. Tools such as the Semantic Analyzer support the development of the data economy and digitisation more broadly and aim to democratise artificial intelligence. Introduction of any AI-based tool requires strong engagement and enthusiasm from the end-user, support by leadership, and, in case of projects that use machine learning, seamless access to the data.

For example, semantic analysis can generate a repository of the most common customer inquiries and then decide how to address or respond to them. The semantic analysis uses two distinct techniques to obtain information from text or corpus of data. The first technique refers to text classification, while the second relates to text extractor. With a semantic analyser, this quantity of data can be treated and go through information retrieval and can be treated, analysed and categorised, not only to better understand customer expectations but also to respond efficiently. Therefore, the goal of semantic analysis is to draw exact meaning or dictionary meaning from the text.

The coverage of Scopus publications are balanced between Health Sciences (32% of total Scopus publication) and Physical Sciences (29% of total Scopus publication). Other approaches include analysis of verbs in order to identify relations on textual data [134–138]. However, the proposed solutions are normally developed for a specific domain or are language dependent. 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].

Instead of merely recommending popular shows or relying on genre tags, NeuraSense’s system analyzes the deep-seated emotions, themes, and character developments that resonate with users. For example, if a user expressed admiration for strong character development in a mystery series, the system might recommend another series with intricate character arcs, even if it’s from a different genre. It was surprising to find the high presence of the Chinese language among the studies. Chinese language is the second most cited language, and the HowNet, a Chinese-English knowledge database, is the third most applied external source in semantics-concerned text mining studies. Looking at the languages addressed in the studies, we found that there is a lack of studies specific to languages other than English or Chinese. We also found an expressive use of WordNet as an external knowledge source, followed by Wikipedia, HowNet, Web pages, SentiWordNet, and other knowledge sources related to Medicine.

The characteristic concepts of each group can be used to give a quick overview of the content covered in each collection. A graphical representation shows which group a text belongs to and thus allows you to find texts that deal with related topics. Alternatively, we can use a set of terms to describe the content we are looking for and find texts with these terms, as well as with terms that we have not mentioned but are close in content (e.g., synonyms, sub-names, super-names). As previously stated, the objective of this systematic mapping is to provide a general overview of semantics-concerned text mining studies.

IBM’s Watson provides a conversation service that uses semantic analysis (natural language understanding) and deep learning to derive meaning from unstructured data. It analyzes text to reveal the type of sentiment, emotion, data category, and the relation between words based on the semantic role of the keywords used in the text. According to IBM, semantic analysis has saved 50% of the company’s time on the information gathering process. Public administrations process many text documents, among which we must find those that speak about a certain topic and need to be reviewed to explain proposals or decisions. Large sets of such essays are no longer capable of being quantitatively, let alone qualitatively, reviewed, understood, and compared by one individual. The tool we created is available freely, in open source, and has already been used in text mining by different groups worldwide.

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. The semantic analysis process begins by studying and analyzing the dictionary definitions and meanings of individual words also referred to as lexical semantics. Following this, the relationship between words in a sentence is examined to provide clear understanding of the context. MonkeyLearn makes it simple for you to get started with automated semantic analysis tools. Using a low-code UI, you can create models to automatically analyze your text for semantics and perform techniques like sentiment and topic analysis, or keyword extraction, in just a few simple steps.

Several different research fields deal with text, such as text mining, computational linguistics, machine learning, information retrieval, semantic web and crowdsourcing. Grobelnik [14] states the importance of an integration of these research areas in order to reach a complete solution to the problem of text understanding. Sentence-level perception and semantic analysis described above can be scaled to paragraphs, chapters, whole texts, and even larger structures, addressing the problem of computational scalability95,148,149. 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.

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As integral part of human cognition, natural language invites correspondingly integral modeling approach8,9,10,11,12,13. Our method of modeling, based on quantum-theoretic conceptual and mathematical structure, is common for various kinds of behavior including natural language14. Search engines like Google heavily rely on semantic analysis to produce relevant search results.

Besides, we can find some studies that do not use any linguistic resource and thus are language independent, as in [57–61]. These facts can justify that English was mentioned in only 45.0% of the considered studies. 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 authors argue that search engines must also be able to find results that are indirectly related to the user’s keywords, considering the semantics and relationships between possible search results. The paper presents quantum model of subjective text perception based on binary cognitive distinctions corresponding to words of natural language. The result of perception is quantum cognitive state represented by vector in the qubit Hilbert space. In the case of two distinctions, the perception model generates a two-qubit state, entanglement of which quantifies semantic connection between the corresponding words.

Today, this method reconciles humans and technology, proposing efficient solutions, notably when it comes to a brand’s customer service. The relatedness of two documents in different languages is assessed by the cosine similarity between the corresponding vector representations. According to calculation of amplitudes described in “Results” section, cognitive model of the text (4) depends on its sentence structure. In particular, random shuffle of words and periods leads to factorization of state (4) and zero concurrence which reflects elimination of semantic connection. At the same time, calculation of amplitudes is not affected by shuffle of both sentences within text and words within sentences, so that subsequent calculation of concurrence as measure of semantic connection is also invariant to these operations. The algorithm thereby treats text as a bag of sentences which may be paralleled with a bag of words level of text analysis146,147.

Semiotics refers to what the word means and also the meaning it evokes or communicates. For example, ‘tea’ refers to a hot beverage, while it also evokes refreshment, alertness, and many other associations. Semantic analysis helps fine-tune the search engine optimization (SEO) strategy by allowing companies to analyze and decode users’ searches. The approach helps deliver optimized and suitable content to the users, thereby boosting traffic and improving result relevance.

Algorithms and measures are used for assessing texts at syntactical and semantic level. An important text-mining method and similarity measure is latent semantic analysis (LSA). It provides for reducing the dimensionality of the document vector space and better capturing the text semantics.

Driven by the analysis, tools emerge as pivotal assets in crafting customer-centric strategies and automating processes. Moreover, they don’t just parse text; they extract valuable information, discerning opposite meanings and extracting relationships between words. Efficiently working behind the scenes, semantic analysis excels in understanding language and inferring intentions, emotions, and context.

It shows that there is a concern about developing richer text representations to be input for traditional machine learning algorithms, as we can see in the studies of [55, 139–142]. 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 semantic analysis of text 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.

Still, rational models of human choice developed in the era of mechanistic worldview hold as important limiting cases of individual and collective behavior18. Translating a sentence isn’t just about replacing words from one language with another; it’s about preserving the original meaning and context. For instance, a direct word-to-word translation might result in grammatically correct sentences that sound unnatural or lose their original intent.

Among the most common problems treated through the use of text mining in the health care and life science is the information retrieval from publications of the field. The search engine PubMed [33] and the MEDLINE database are the main text sources among these studies. There are also studies related to the extraction of events, genes, proteins and their associations [34–36], detection of adverse drug reaction [37], and the extraction of cause-effect and disease-treatment relations [38–40]. 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.

Chatbots, virtual assistants, and recommendation systems benefit from semantic analysis by providing more accurate and context-aware responses, thus significantly improving user satisfaction. Search engines can provide more relevant results by understanding user queries better, considering the context and meaning rather than just keywords. 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.

In simple words, we can say that lexical semantics represents the relationship between lexical items, the meaning of sentences, and the syntax of the sentence. It is the first part of semantic analysis, in which we study the meaning of individual words. It involves words, sub-words, affixes (sub-units), compound words, and phrases also. Semantic analysis systems are used by more than just B2B and B2C companies to improve the customer experience.