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

With the help of semantic analysis, machine learning tools can recognize a ticket either as a “Payment issue” or a“Shipping problem”. 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. Consider the task of text summarization which is used to create digestible chunks of information from large quantities of text.

The data representation must preserve the patterns hidden in the documents in a way that they can be discovered in the next step. In the pattern extraction step, the analyst applies a suitable algorithm to extract the hidden patterns. The algorithm is chosen based on the data available and the type of pattern semantic text analysis that is expected. If this knowledge meets the process objectives, it can be put available to the users, starting the final step of the process, the knowledge usage. Otherwise, another cycle must be performed, making changes in the data preparation activities and/or in pattern extraction parameters.

Analyzing the meaning of the client’s words is a golden lever, deploying operational improvements and bringing services to the clientele. However, literary analysis doesn’t just involve discovering the author’s intended meaning. It often also explores potentially unintended connections between different texts, asks what a text reveals about the context in which it was written, or seeks to analyze a classic text in a new and unexpected way. Almost all work in this field involves in-depth analysis of texts – in this context, usually novels, poems, stories or plays.

NLP Libraries

Hence, it is critical to identify which meaning suits the word depending on its usage. Insights derived from data also help teams detect areas of improvement and make better decisions. For example, you might decide to create a strong knowledge base by identifying the most common customer inquiries. The automated process of identifying in which sense is a word used according to its context. 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).

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. Although several researches have been developed in the text mining field, the processing of text semantics remains an open research problem. 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. We found considerable differences in numbers of studies among different languages, since 71.4% of the identified studies deal with English and Chinese. Thus, there is a lack of studies dealing with texts written in other languages.

Products and services

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.

In this phase, information about each study was extracted mainly based on the abstracts, although some information was extracted from the full text. All in all, semantic analysis enables chatbots to focus on user needs and address their queries in lesser time and lower cost. Chatbots help customers immensely as they facilitate shipping, answer queries, and also offer personalized guidance and input on how to proceed further. Moreover, some chatbots are equipped with emotional intelligence that recognizes the tone of the language and hidden sentiments, framing emotionally-relevant responses to them. 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.

From enhancing Business Intelligence to refining Semantic Search capabilities, the impact of this advanced interpretative approach is far-reaching and continues to grow. Ultimately, the burgeoning field of Semantic Technology continues to advance, bringing forward enhanced capabilities for professionals to harness. These Semantic Analysis Tools are not just technological marvels but partners in your analytical quests, assisting in transforming unstructured text into structured knowledge, one byte at a time. Together, these technologies forge a potent combination, empowering you to dissect and interpret complex information seamlessly. Whether you’re looking to bolster business intelligence, enrich research findings, or enhance customer engagement, these core components of Semantic Text Analysis offer a strategic advantage.

• 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.
• Also, ‘smart search‘ is another functionality that one can integrate with ecommerce search tools.

It is a crucial component of Natural Language Processing (NLP) and the inspiration for applications like chatbots, search engines, and text analysis using machine learning. The Natural Language Understanding Evolution is an exciting frontier in the realm of text analytics, with implications that span across various sectors from healthcare to customer service. Innovations in machine learning and cognitive computing are leading to NLP systems with greater sophistication—ones that can understand context, colloquialisms, and even complex emotional nuances within language.

Additionally, it delves into the contextual understanding and relationships between linguistic elements, enabling a deeper comprehension of textual content. A detailed literature review, as the review of Wimalasuriya and Dou [17] (described in “Surveys” section), would be worthy for organization and summarization of these specific research subjects. Among these methods, we can find named entity recognition (NER) and semantic role labeling. 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]. The distribution of text mining tasks identified in this literature mapping is presented in Fig.

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. 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.

IBM’s Watson conversation service

When looking at the external knowledge sources used in semantics-concerned text mining studies (Fig. 7), WordNet is the most used source. This lexical resource is cited by 29.9% of the studies that uses information beyond the text data. WordNet can be used to create or expand the current set of features for subsequent text classification or clustering.

The tool analyzes every user interaction with the ecommerce site to determine their intentions and thereby offers results inclined to those intentions. 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).

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. 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.

Effectively, support services receive numerous multichannel requests every day. You can foun additiona information about ai customer service and artificial intelligence and NLP. Textual analysis is a broad term for various research methods used to describe, interpret and understand texts. All kinds of information can be gleaned from a text – from its literal meaning to the subtext, symbolism, assumptions, and values it reveals.

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. The Development of Semantic Models is an ever-evolving process aimed at refining the accuracy and efficacy with which complex textual data is analyzed. By harnessing the power of machine learning and artificial intelligence, researchers and developers are working tirelessly to advance the subtlety and range of semantic analysis tools.

Advantages of semantic analysis

However, there is a lack of studies that integrate the different branches of research performed to incorporate text semantics in the text mining process. Secondary studies, such as surveys and reviews, can integrate and organize the studies that were already developed and guide future works. 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. In short, sentiment analysis can streamline and boost successful business strategies for enterprises. As discussed earlier, semantic analysis is a vital component of any automated ticketing support.

It begins with raw text data, which encounters a series of sophisticated processes before revealing valuable insights. If you’re ready to leverage the power of semantic analysis in your projects, understanding the workflow is pivotal. Let’s walk you through the integral steps to transform unstructured text into structured wisdom. While Semantic Analysis concerns itself with meaning, Syntactic Analysis is all about structure.

Critical elements of semantic analysis

Capturing the information is the easy part but understanding what is being said (and doing this at scale) is a whole different story. Semantic analysis enables these systems to comprehend user queries, leading to more accurate responses and better conversational experiences. Semantic analysis allows for a deeper understanding of user preferences, enabling personalized recommendations in e-commerce, content curation, and more.

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. In this subsection, we present a consolidation of our results and point some future trends of semantics-concerned text mining. The second most used source is Wikipedia [73], which covers a wide range of subjects and has the advantage of presenting the same concept in different languages.

It helps understand the true meaning of words, phrases, and sentences, leading to a more accurate interpretation of text. Indeed, discovering a chatbot capable of understanding emotional intent or a voice bot’s discerning tone might seem like a sci-fi concept. Semantic analysis, the engine behind these advancements, dives into the meaning embedded in the text, unraveling emotional nuances and intended messages.

These resources can be used for enrichment of texts and for the development of language specific methods, based on natural language processing. Stavrianou et al. [15] present a survey of semantic issues of text mining, which are originated from natural language particularities. This is a good survey focused on a linguistic point of view, rather than focusing only on statistics.

Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text. This technique is used separately or can be used along with one of the above methods to gain more valuable insights. For Example, Tagging Twitter mentions by sentiment to get a sense of how customers feel about your product and can identify unhappy customers in real-time. With the help of meaning representation, we can link linguistic elements to non-linguistic elements. Both polysemy and homonymy words have the same syntax or spelling but the main difference between them is that in polysemy, the meanings of the words are related but in homonymy, the meanings of the words are not related.

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. Another technique in this direction that is commonly used for topic modeling is latent Dirichlet allocation (LDA) [121]. The topic model obtained by LDA has been used for representing text collections as in [58, 122, 123]. Wimalasuriya and Dou [17] present a detailed literature review of ontology-based information extraction.

Word Sense Disambiguation involves interpreting the meaning of a word based upon the context of its occurrence in a text. Semantic analysis aids in analyzing and understanding customer queries, helping to provide more accurate and efficient support. By integrating Semantic Text Analysis into their core operations, businesses, search engines, and academic institutions are all able to make sense of the torrent of textual information at their fingertips. This not only facilitates smarter decision-making, but it also ushers in a new era of efficiency and discovery. Embarking on Semantic Text Analysis requires robust Semantic Analysis Tools and resources, which are essential for professionals and enthusiasts alike to decipher the intricate patterns and meanings in text. The availability of various software applications, online platforms, and extensive libraries enables you to perform complex semantic operations with ease, allowing for a deep dive into the realm of Semantic Technology.

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. The semantic analysis method begins with a language-independent step of analyzing the set of words in the text to understand their meanings. This step is termed ‘lexical semantics‘ and refers to fetching the dictionary definition for the words in the text.

Moreover, the system can prioritize or flag urgent requests and route them to the respective customer service teams for immediate action with semantic analysis. 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. Moreover, granular insights derived from the text allow teams to identify the areas with loopholes and work on their improvement on priority.

Semantics is a branch of linguistics, which aims to investigate the meaning of language. Semantics deals with the meaning of sentences and words as fundamentals in the world. The overall results of the study were that semantics is paramount in processing natural languages and aid in machine learning. This study has covered various aspects including the Natural Language Processing (NLP), Latent Semantic Analysis (LSA), Explicit Semantic Analysis (ESA), and Sentiment Analysis (SA) in different sections of this study. However, LSA has been covered in detail with specific inputs from various sources. This study also highlights the weakness and the limitations of the study in the discussion (Sect. 4) and results (Sect. 5).

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. As an example, in the pre-processing step, the user can provide additional information to define a stoplist and support feature selection. 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.

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. According to a 2020 survey by Seagate technology, around 68% of the unstructured and text data that flows into the top 1,500 global companies (surveyed) goes unattended and unused. With growing NLP and NLU solutions across industries, deriving insights from such unleveraged data will only add value to the enterprises. You understand that a customer is frustrated because a customer service agent is taking too long to respond. In the dynamic landscape of customer service, staying ahead of the curve is not just a…

This is how to use the tf-idf to indicate the importance of words or terms inside a collection of documents. In reference to the above sentence, we can check out tf-idf scores for a few words within this sentence. LSA is an information retrieval technique which analyzes and identifies the pattern in unstructured collection of text and the relationship between them. The idea of entity extraction is to identify named entities in text, such as names of people, companies, places, etc. In Sentiment analysis, our aim is to detect the emotions as positive, negative, or neutral in a text to denote urgency.

Relatedly, it’s good to be careful of confirmation bias when conducting these sorts of analyses, grounding your observations in clear and plausible ways. Parsing implies pulling out a certain set of words from a text, based on predefined rules. For example, we want to find out the names of all locations mentioned in a newspaper. Semantic analysis would be an overkill for such an application and syntactic analysis does the job just fine. While semantic analysis is more modern and sophisticated, it is also expensive to implement. Content is today analyzed by search engines, semantically and ranked accordingly.

Named Entity Recognition (NER) is a technique that reads through text and identifies key elements, classifying them into predetermined categories such as person names, organizations, locations, and more. NER helps in extracting structured information from unstructured text, facilitating data analysis in fields ranging from journalism to legal case management. The landscape of Text Analytics has been reshaped by Machine Learning, providing dynamic capabilities in pattern recognition, anomaly detection, and predictive insights.

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]. 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.

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. Powerful semantic-enhanced machine learning tools will deliver valuable insights that drive better decision-making and improve customer experience. Using Syntactic analysis, a computer would be able to understand the parts of speech of the different words in the sentence. Based on the understanding, it can then try and estimate the meaning of the sentence. In the case of the above example (however ridiculous it might be in real life), there is no conflict about the interpretation.

The concept-based semantic exploitation is normally based on external knowledge sources (as discussed in the “External knowledge sources” section) [74, 124–128]. As an example, explicit semantic analysis [129] rely on Wikipedia to represent the documents by a concept vector. In a similar way, Spanakis et al. [125] improved hierarchical clustering quality by using a text representation based on concepts and other Wikipedia features, such as links and categories.

Semantic-enhanced machine learning tools are vital natural language processing components that boost decision-making and improve the overall customer experience. Thanks to machine learning and natural language processing (NLP), semantic analysis includes the work of reading and sorting relevant interpretations. Artificial intelligence contributes to providing better solutions to customers when they contact customer service. These proposed solutions are more precise and help to accelerate resolution times. Semantics gives a deeper understanding of the text in sources such as a blog post, comments in a forum, documents, group chat applications, chatbots, etc.