Text & Semantic Analysis Machine Learning with Python by SHAMIT BAGCHI

Semantic analysis is key to contextualization that helps disambiguate language data so text-based NLP applications can be more accurate. 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. 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.

It is thus important to load the content with sufficient context and expertise. On the whole, such a trend has improved the general content quality of the internet. You can make your own mind up about that this semantic divergence signifies.

• This provides a foundational overview of how semantic analysis works, its benefits, and its core components.
• One can train machines to make near-accurate predictions by providing text samples as input to semantically-enhanced ML algorithms.
• These proposed solutions are more precise and help to accelerate resolution times.

The next most useful feature selected by Chi-square test is “great”, I assume it is from mostly the positive reviews. In reference to the above sentence, we can check out tf-idf scores for a few words within this sentence. LSA itself is an unsupervised way of uncovering synonyms in a collection of documents. LSA is an information retrieval technique which analyzes and identifies the pattern in unstructured collection of text and the relationship between them. Semantic analysis, on the other hand, is crucial to achieving a high level of accuracy when analyzing text.

Interesting topics

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. Thus, the search terms of a systematic mapping are broader and the results are usually presented through graphs. 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.

Suppose we had 100 articles and 10,000 different terms (just think of how many unique words there would be all those articles, from “amendment” to “zealous”!). When we start to break our data down into the 3 components, we can actually choose the number of topics — we could choose to have 10,000 different topics, if we genuinely thought that was reasonable. However, we could probably represent the data with far fewer topics, let’s say the 3 we originally talked about. That means that in our document-topic table, we’d slash about 99,997 columns, and in our term-topic table, we’d do the same. The columns and rows we’re discarding from our tables are shown as hashed rectangles in Figure 6. The relatedness of two documents in different languages is assessed by the cosine similarity between the corresponding vector representations.

Natural Language Processing, Editorial, Programming

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 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 use of features based on WordNet has been applied with and without good results [55, 67–69]. Besides, WordNet can support the computation of semantic similarity [70, 71] and the evaluation of the discovered knowledge [72].

Further depth can be added to each section based on the target audience and the article’s length. Whether it is Siri, Alexa, or Google, they can all understand human language (mostly). Today we will be exploring how some of the latest developments in NLP (Natural Language Processing) can make it easier for us to process and analyze text. Semantic analysis systems are used by more than just B2B and B2C companies to improve the customer experience.

By using semantic analysis tools, concerned business stakeholders can improve decision-making and customer experience. Apart from these vital elements, the semantic analysis also uses semiotics and collocations to understand and interpret language. Semiotics refers to what the word means and also the meaning it evokes or communicates.

The meaning representation can be used to reason for verifying what is correct in the world as well as to extract the knowledge with the help of semantic representation. Therefore, the goal of semantic analysis is to draw exact meaning or dictionary meaning from the text. Effectively, support services receive numerous multichannel requests every day. 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. 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.

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. Chatbots help customers immensely as they facilitate shipping, answer queries, and also offer personalized guidance and input on how to proceed further.

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. Semantic analysis significantly improves language understanding, enabling machines to process, analyze, and generate text with greater accuracy and context sensitivity. Indeed, semantic analysis is pivotal, fostering better user experiences and enabling more efficient information retrieval and processing. The first step of a systematic review or systematic mapping study is its planning.

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. The automated process of identifying in which sense is a word used according to its context. Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive

positive feedback from the reviewers.

Now moving to the right in our diagram, the matrix M is applied to this vector space and this transforms it into the new, transformed space in our top right corner. In the diagram below the geometric effect of M would be referred to as “shearing” the vector space; the two vectors 𝝈1 and 𝝈2 are actually our singular values plotted in this space. You’ll notice that our two tables have one thing in common (the documents / articles) and all three of them have one thing in common — the topics, or some representation of them.

• They declared that the systems submitted to those challenges use cross-pair similarity measures, machine learning, and logical inference.
• 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.
• Stavrianou et al. [15] present a survey of semantic issues of text mining, which are originated from natural language particularities.
• In semantic analysis, relationships include various entities, such as an individual’s name, place, company, designation, etc.
• By using semantic analysis tools, concerned business stakeholders can improve decision-making and customer experience.

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. Expert.ai’s rule-based technology starts by reading all of the words within a piece of content to capture its real meaning. It then identifies the textual elements and assigns them to their logical and grammatical roles. Finally, it analyzes the surrounding text and text structure to accurately determine the proper meaning of the words in context. 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.

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. 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.

Bos [31] presents an extensive survey of computational semantics, a research area focused on computationally understanding human language in written or spoken form. He discusses how to represent semantics in order to capture the meaning of human language, how to construct these representations from natural language expressions, and how to draw inferences from the semantic representations. The author also discusses the generation of background knowledge, which can support reasoning tasks.

Thus, this paper reports a systematic mapping study to overview the development of semantics-concerned studies and fill a literature review gap in this broad research field through a well-defined review process. Semantics can be related to a vast number of subjects, and most of them are studied in the natural language processing field. As examples of semantics-related subjects, we can mention representation of meaning, semantic parsing and interpretation, word sense disambiguation, and coreference resolution. Nevertheless, the focus of this paper is not on semantics but on semantics-concerned text mining studies.

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. If any changes in the stated objectives or selected text collection must be made, the text mining process should be restarted at the problem identification step.

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. MedIntel, a global health tech company, launched a patient feedback system in 2023 that uses a semantic analysis process to improve patient care.

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. 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.

Calculating the outer product of two vectors with shapes (m,) and (n,) would give us a matrix with a shape (m,n). In other words, every possible product of any two numbers in the two vectors is computed and placed in the new matrix. The singular value not only weights the sum but orders it, since the values are arranged in descending order, so that the first singular value is always the highest one. We can arrive at the same understanding of PCA if we imagine that our matrix M can be broken down into a weighted sum of separable matrices, as shown below. What matters in understanding the math is not the algebraic algorithm by which each number in U, V and 𝚺 is determined, but the mathematical properties of these products and how they relate to each other.

It is also a key component of several machine learning tools available today, such as search engines, chatbots, and text analysis software. Whether using machine learning or statistical techniques, the text mining approaches are usually language independent. However, specially in the natural language processing field, annotated corpora is often required to train models in order to resolve a certain task for each specific language (semantic role labeling problem is an example).

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. The use of Wikipedia is followed by the use of the Chinese-English knowledge database HowNet [82].

If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples. Compare your paper to billions of pages and articles with Scribbr’s Turnitin-powered plagiarism checker. To know the meaning of Orange in a sentence, we need to know the words around it. The values in 𝚺 represent how much each latent concept explains the variance in our data. When these are multiplied by the u column vector for that latent concept, it will effectively weigh that vector. The matrices 𝐴𝑖 are said to be separable because they can be decomposed into the outer product of two vectors, weighted by the singular value 𝝈i.

TF-IDF is an information retrieval technique that weighs a term’s frequency (TF) and its inverse document frequency (IDF). The product of the TF and IDF scores of a word is called the TFIDF weight of that word. Capturing the information is the easy part but understanding what is being said (and doing this at scale) is a whole different story. In-Text Classification, our aim is to label the text according to the insights we intend to gain from the textual data.

We can any of the below two semantic analysis techniques depending on the type of information you would like to obtain from the given data. 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. As we discussed, the most important task of semantic analysis is to find the proper meaning of the sentence. This provides a foundational overview of how semantic analysis works, its benefits, and its core components.

If we’re looking at foreign policy, we might see terms like “Middle East”, “EU”, “embassies”. For elections it might be “ballot”, “candidates”, “party”; and for reform we might see “bill”, “amendment” or “corruption”. text semantic analysis So, if we plotted these topics and these terms in a different table, where the rows are the terms, we would see scores plotted for each term according to which topic it most strongly belonged.

Rather than using traditional feedback forms with rating scales, patients narrate their experience in natural language. You can foun additiona information about ai customer service and artificial intelligence and NLP. MedIntel’s system employs semantic analysis to extract critical aspects of patient feedback, such as concerns about medication side effects, appreciation for specific caregiving techniques, or issues with hospital facilities. By understanding the underlying sentiments and specific issues, hospitals and clinics can tailor their services more effectively to patient needs. 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.

Besides the vector space model, there are text representations based on networks (or graphs), which can make use of some text semantic features. Network-based representations, such as bipartite networks and co-occurrence networks, can represent relationships between terms or between documents, which is not possible through the vector space model [147, 156–158]. We also found some studies that use SentiWordNet [92], which is a lexical resource for sentiment analysis and opinion mining [93, 94].

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]. The authors present an overview of relevant aspects in textual entailment, discussing four PASCAL Recognising Textual Entailment (RTE) Challenges.

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. It also shortens response time considerably, which keeps customers satisfied and happy. Relationship extraction is a procedure used to determine the semantic relationship between words in a text.

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. In the social sciences, textual analysis is often applied to texts such as interview transcripts and surveys, as well as to various types of media. Social scientists use textual data to draw empirical conclusions about social relations. 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. Content is today analyzed by search engines, semantically and ranked accordingly.

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. 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. Upon parsing, the analysis then proceeds to the interpretation step, which is critical for artificial intelligence algorithms.

This module covers the basics of the language, before looking at key areas such as document structure, links, lists, images, forms, and more. In the case of syntactic analysis, the syntax of a sentence is used to interpret a text. In the case of semantic analysis, the overall context of the text is considered during the analysis.

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. 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.

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. The application of natural language processing methods (NLP) is also frequent. Among these methods, we can find named entity recognition (NER) and semantic role labeling.

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).

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. Word sense disambiguation can contribute to a better document representation. It is normally based on external knowledge sources and can also be based on machine learning methods [36, 130–133].