Understanding Semantic Analysis Using Python - NLP Towards AI

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].

A word cloud3 of methods and algorithms identified in this literature mapping is presented in Fig. 9, in which the font size reflects the frequency of the methods and algorithms among the accepted papers. We can note that the most common approach deals with latent semantics through Latent Semantic Indexing (LSI) [2, 120], a method that can be used for data dimension reduction and that is also known as latent semantic analysis. In this semantic space, alternative forms expressing the same concept are projected to a common representation. It reduces the noise caused by synonymy and polysemy; thus, it latently deals with text semantics.

The process starts with the specification of its objectives in the problem identification step. The text mining analyst, preferably working along with a domain expert, must delimit the text mining application scope, including the text collection that will be mined and how the result will be used. Customers benefit from such a support system as they receive timely and accurate responses on the issues raised by them. Moreover, the system can prioritize or flag urgent requests and route them to the respective customer service teams for immediate action with semantic analysis. All factors considered, Uber uses semantic analysis to analyze and address customer support tickets submitted by riders on the Uber platform.

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.

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.

For example, the word ‘Blackberry’ could refer to a fruit, a company, or its products, along with several other meanings. Moreover, context is equally important while processing the language, as it takes into account the environment of the sentence and then attributes the correct meaning to it. For us humans, there is nothing more simple than recognising the meaning of a sentence based on the punctuation or intonation used. In this way, quantum approach allows to consider simple units of cognition while circumventing detailed description of the human’s mind and brain. At this level of modeling, numerous intricacies of human cognition are hidden, but continue to affect observable behavior (cf.76). Further sections illustrate this modeling approach on the process of subjective text perception.

• Health care and life sciences is the domain that stands out when talking about text semantics in text mining applications.
• We do not present the reference of every accepted paper in order to present a clear reporting of the results.
• Bos [31] indicates machine learning, knowledge resources, and scaling inference as topics that can have a big impact on computational semantics in the future.
• Semiotics refers to what the word means and also the meaning it evokes or communicates.
• This allows to account for contextual cognitive and behavioral phenomena by simple and quantitative models reviewed in15,26,27.

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.

Language Modeling

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.

• Insights derived from data also help teams detect areas of improvement and make better decisions.
• Search engines like Google heavily rely on semantic analysis to produce relevant search results.
• 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.
• Nevertheless, it is also an interactive process, and there are some points where a user, normally a domain expert, can contribute to the process by providing his/her previous knowledge and interests.

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.

Advantages of semantic analysis

These proposed solutions are more precise and help to accelerate resolution times. In our model, cognition of a subject is based on a set of linguistically expressed concepts, e.g. apple, face, sky, functioning as high-level cognitive units organizing perceptions, memory and reasoning of humans77,78. As stated above, these units exemplify cogs encoded by distributed neuronal ensembles66.

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).

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.

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 semantic analysis of text 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.

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.

What is Call Center Knowledge Base and How to Build It? 2024 Updated

Since the number of even single-word concepts in cognition of adult human is very large, each concept is passive most of the time, but may be activated by internal or external stimuli acquired e.g. from verbal or visual channels. This paper considers a particular class of such stimuli which are texts in natural language. Despite many promising results, quantum approach to human cognition and language modeling is still in a formation stage. You can foun additiona information about ai customer service and artificial intelligence and NLP. A number of quantum-theoretic concepts and features stay unused, including complex-valued calculus of state representations, entanglement of multipartite systems, and methods for their analysis.

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.

In the realm of customer support, automated ticketing systems leverage semantic analysis to classify and prioritize customer complaints or inquiries. When a customer submits a ticket saying, “My app crashes every time I try to login,” semantic analysis helps the system understand the criticality of the issue (app crash) and its context (during login). As a result, tickets can be automatically categorized, prioritized, and sometimes even provided to customer service teams with potential solutions without human intervention.

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.

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.

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 application domain is followed by the Web domain, what can be explained by the constant growth, in both quantity and coverage, of Web content. 9, we can observe the predominance of traditional machine learning algorithms, such as Support Vector Machines (SVM), Naive Bayes, K-means, and k-Nearest Neighbors (KNN), in addition to artificial neural networks and genetic algorithms. Among these methods, we can find named entity recognition (NER) and semantic role labeling.

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.

Semantic analysis has firmly positioned itself as a cornerstone in the world of natural language processing, ushering in an era where machines not only process text but genuinely understand it. As we’ve seen, from chatbots enhancing user interactions to sentiment analysis decoding the myriad emotions within textual data, the impact of semantic data analysis alone is profound. As technology continues to evolve, one can only anticipate even deeper integrations and innovative applications.

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. 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. The automated process of identifying in which sense is a word used according to its context.

According to psycho-physiological parallelism54, modern cognitive science builds on fusion of physical and information descriptions outlined above, constituting complementary sides of the same phenomena55,56,57,58,59,60,61,62,63. In this approach, firing frequency of distributed ensembles of neurons functions as a code of cognitive algorithms and signals64,65. Detailed correspondence between these cognitive and physiological perspectives is established by dual-network representation of cognitive entities and neural patterns that encode them59,66,67. Same phenomena can be described in information terms such that action potentials are considered as signals linking binary neural registers while total activity of the nervous system is referred to as psyche, cognition or mind51,52.

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.

Bharathi and Venkatesan [18] present a brief description of several studies that use external knowledge sources as background knowledge for document clustering. Reshadat and Feizi-Derakhshi [19] present several semantic similarity measures based on external knowledge sources (specially WordNet and MeSH) and a review of comparison results from previous studies. The prototype enables easy and efficient algorithmic processing of large corpuses of documents and texts with finding content similarities using advanced grouping and visualisation. A web tool supporting natural language (like legislation, public tenders) is planned to be developed. Today, machine learning algorithms and NLP (natural language processing) technologies are the motors of semantic analysis tools. Deep similarity between quantum physical processes and cognitive practice of humans is a fundamental advantage of quantum approach in natural language modeling.

The application of text mining methods in information extraction of biomedical literature is reviewed by Winnenburg et al. [24]. The paper describes the state-of-the-art text mining approaches for supporting manual text annotation, such as ontology learning, named entity and concept identification. They also describe and compare biomedical search engines, in the context of information retrieval, literature retrieval, result processing, knowledge retrieval, semantic processing, and integration of external tools.

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 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.

This allows Cdiscount to focus on improving by studying consumer reviews and detecting their satisfaction or dissatisfaction with the company’s products. One can train machines to make near-accurate predictions by providing text samples as input to semantically-enhanced ML algorithms. Machine learning-based semantic analysis involves sub-tasks such as relationship extraction and word sense disambiguation. The semantic analysis method begins with a language-independent step of analyzing the set of words in the text to understand their meanings.

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].

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.

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.

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.

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.

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 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.

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.

The conduction of this systematic mapping followed the protocol presented in the last subsection and is illustrated in Fig. The selection and the information extraction phases were performed with support of the Start tool [13]. As discussed earlier, semantic analysis is a vital component of any automated ticketing support.