An Introduction to Natural Language Processing NLP

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. In empirical research, researchers use to execute several experiments in order to evaluate proposed methods and algorithms, which would require the involvement of several users, therefore making the evaluation not feasible in practical ways. We also found some studies that use SentiWordNet [92], which is a lexical resource for sentiment analysis and opinion mining [93, 94]. Among other external sources, we can find knowledge sources related to Medicine, like the UMLS Metathesaurus [95–98], MeSH thesaurus [99–102], and the Gene Ontology [103–105]. Besides the top 2 application domains, other domains that show up in our mapping refers to the mining of specific types of texts.

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]. 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]. 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.

How is Semantic Analysis different from Lexical Analysis?

In the form of chatbots, natural language processing can take some of the weight off customer service teams, promptly responding to online queries and redirecting customers when needed. NLP can also analyze customer surveys and feedback, allowing teams to gather timely intel on how customers feel about a brand and steps they can take to improve customer sentiment. Relationship extraction takes the named entities of NER and tries to identify the semantic relationships between them. This could mean, for example, finding out who is married to whom, that a person works for a specific company and so on.

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.

About this paper

Several surveys have been published to analyze diverse approaches for the traditional text classification methods. Most of these surveys cover application of different semantic term relatedness methods semantic analysis of text in text classification up to a certain degree. However, they do not specifically target semantic text classification algorithms and their advantages over the traditional text classification.

• The field lacks secondary studies in areas that has a high number of primary studies, such as feature enrichment for a better text representation in the vector space model.
• In that way, hierarchical semantic structure of information representation, typical to human cognition9,150, can be accessed.
• Moreover, granular insights derived from the text allow teams to identify the areas with loopholes and work on their improvement on priority.
• Semantic analysis methods will provide companies the ability to understand the meaning of the text and achieve comprehension and communication levels that are at par with humans.

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. Words are treated as string sequences in these kinds of textual data representations. The main logic behind the algorithms in this category depends on a word/character sequence taken out from documents by ordinary string-matching method. N-gram based demonstration (Cavnar & Trenkle, 1994) and similar works in Ho and Funakoshi (1998), Ho and Nguyen (2000) and Fung (2003) are traditional examples of these types of systems.

Word Sense Disambiguation:

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.

As well as WordNet, HowNet is usually used for feature expansion [83–85] and computing semantic similarity [86–88]. 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 approach helps deliver optimized and suitable content to the users, thereby boosting traffic and improving result relevance. 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. The use of Wikipedia is followed by the use of the Chinese-English knowledge database HowNet [82]. Finding HowNet as one of the most used external knowledge source it is not surprising, since Chinese is one of the most cited languages in the studies selected in this mapping (see the “Languages” section).

Maps are essential to Uber’s cab services of destination search, routing, and prediction of the estimated arrival time (ETA). Along with services, it also improves the overall experience of the riders and drivers. To learn more and launch your own customer self-service project, get in touch with our experts today.

Semantic analysis plays a vital role in the automated handling of customer grievances, managing customer support tickets, and dealing with chats and direct messages via chatbots or call bots, among other tasks. For example, ‘Raspberry Pi’ can refer to a fruit, a single-board computer, or even a company (UK-based foundation). Hence, it is critical to identify which meaning suits the word depending on its usage. Besides, going even deeper in the interpretation of the sentences, we can understand their meaning—they are related to some takeover—and we can, for example, infer that there will be some impacts on the business environment. Capturing the information is the easy part but understanding what is being said (and doing this at scale) is a whole different story. The automated process of identifying in which sense is a word used according to its context.

The platform allows Uber to streamline and optimize the map data triggering the ticket. This degree of language understanding can help companies automate even the most complex language-intensive processes and, in doing so, transform the way they do business. So the question is, why settle for an educated guess when you can rely on actual knowledge? Automatically classifying tickets using semantic analysis tools alleviates agents from repetitive tasks and allows them to focus on tasks that provide more value while improving the whole customer experience. I will explore a variety of commonly used techniques in semantic analysis and demonstrate their implementation in Python. By covering these techniques, you will gain a comprehensive understanding of how semantic analysis is conducted and learn how to apply these methods effectively using the Python programming language.

We found research studies in mining news, scientific papers corpora, patents, and texts with economic and financial content. The advantage of a systematic literature review is that the protocol clearly specifies its bias, since the review process is well-defined. However, it is possible to conduct it in a controlled and well-defined way through a systematic process. B2B and B2C companies are not the only ones to deploy systems of semantic analysis to optimize the customer experience. Google developed its own semantic tool to improve the understanding of user searchers.

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. Hence, under Compositional Semantics Analysis, we try to understand how combinations of individual words form the meaning of the text. Also, ‘smart search‘ is another functionality that one can integrate with ecommerce search tools. The tool analyzes every user interaction with the ecommerce site to determine their intentions and thereby offers results inclined to those intentions. A ‘search autocomplete‘ functionality is one such type that predicts what a user intends to search based on previously searched queries. It saves a lot of time for the users as they can simply click on one of the search queries provided by the engine and get the desired result.

“Single-concept perception”, “Two-concept perception”, “Entanglement measure of semantic connection” sections describe a model of subjective text perception and semantic relation between the resulting cognitive entities. 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. 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. Thus, as we already expected, health care and life sciences was the most cited application domain among the literature accepted studies.

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. 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. Today, machine learning algorithms and NLP (natural language processing) technologies are the motors of semantic analysis tools. Text mining studies steadily gain importance in recent years due to the wide range of sources that produce enormous amounts of data, such as social networks, blogs/forums, web sites, e-mails, and online libraries publishing research papers.