Latent Semantic Analysis: intuition, math, implementation by Ioana

Thus, due to limitations of time and resources, the mapping was mainly performed based on abstracts of papers. Nevertheless, we believe that our limitations do not have a crucial impact on the results, since our study has a broad coverage. 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.

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 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. This paper reports a systematic mapping study conducted to get a general overview of how text semantics is being treated in text mining studies. It fills a literature review gap in this broad research field through a well-defined review process. As a systematic mapping, our study follows the principles of a systematic mapping/review.

Therefore, the reader can miss in this systematic mapping report some previously known studies. It is not our objective to present a detailed survey of every specific topic, method, or text mining task. This systematic mapping is a starting point, and surveys with a narrower focus should be conducted for reviewing the literature of specific subjects, according to one’s interests.

Concepts

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. Traditionally, text mining techniques are based on both a bag-of-words representation and application of data mining techniques. In order to get a more complete analysis of text collections and get better text mining results, several researchers directed their attention to text semantics. In semantic analysis with machine learning, computers use word sense disambiguation to determine which meaning is correct in the given context.

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 text semantic analysis a similar kind. Semantic analysis is defined as a process of understanding natural language (text) by extracting insightful information such as context, emotions, and sentiments from unstructured data. This article explains the fundamentals of semantic analysis, how it works, examples, and the top five semantic analysis applications in 2022.

Semantic Analysis: What Is It, How & Where To Works

Syntactic analysis involves analyzing the grammatical syntax of a sentence to understand its meaning. Accuracy has dropped greatly for both, but notice how small the gap between the models is! Our LSA model is able to capture about as much information from our test data as our standard model did, with less than half the dimensions! Since this is a multi-label classification it would be best to visualise this with a confusion matrix (Figure 14). Our results look significantly better when you consider the random classification probability given 20 news categories. If you’re not familiar with a confusion matrix, as a rule of thumb, we want to maximise the numbers down the diagonal and minimise them everywhere else.

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 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 semantic analysis technology behind these solutions provides a better understanding of users and user needs. These solutions can provide instantaneous and relevant solutions, autonomously and 24/7. The analysis of the data is automated and the customer service teams can therefore concentrate on more complex customer inquiries, which require human intervention and understanding. Further, digitised messages, received by a chatbot, on a social network or via email, can be analyzed in real-time by machines, improving employee productivity. Uber uses semantic analysis to analyze users’ satisfaction or dissatisfaction levels via social listening.

Figure 10 presents types of user’s participation identified in the literature mapping studies. Besides that, users are also requested to manually annotate or provide a few labeled data [166, 167] or generate of hand-crafted rules [168, 169]. 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 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].

LSA for Exploratory Data Analysis (EDA)

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. 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. 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.

This paper aims to point some directions to the reader who is interested in semantics-concerned text mining researches. 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. Although several researches have been developed in the text mining field, the processing of text semantics remains an open research problem.

The methods used to conduct textual analysis depend on the field and the aims of the research. It often aims to connect the text to a broader social, political, cultural, or artistic context. Relatedly, it’s good to be careful of confirmation bias when conducting these sorts of analyses, grounding your observations in clear and plausible ways. Therefore, in semantic analysis with machine learning, computers use Word Sense Disambiguation to determine which meaning is correct in the given context.

Top Applications of Semantic Analysis

Wikipedia concepts, as well as their links and categories, are also useful for enriching text representation [74–77] or classifying documents [78–80]. In AI and machine learning, semantic analysis helps in feature extraction, sentiment analysis, and understanding relationships in data, which enhances the performance of models. As we enter the era of ‘data explosion,’ it is vital for organizations to optimize this excess yet valuable data and derive valuable insights to drive their business goals.

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. 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.

You can proactively get ahead of NLP problems by improving machine language understanding. Beyond just understanding words, it deciphers complex customer inquiries, unraveling the intent behind user searches and guiding customer service teams towards more effective responses. 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. 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. These chatbots act as semantic analysis tools that are enabled with keyword recognition and conversational capabilities.

The authors also discuss some existing text representation approaches in terms of features, representation model, and application task. The set of different approaches to measure the similarity between documents is also presented, categorizing the similarity measures by type (statistical or semantic) and by unit (words, phrases, vectors, or hierarchies). Sentiment analysis, a subset of semantic analysis, dives deep into textual data to gauge emotions and sentiments.

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. 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. In the realm of customer support, automated ticketing systems leverage semantic analysis to classify and prioritize customer complaints or inquiries.

This proficiency goes beyond comprehension; it drives data analysis, guides customer feedback strategies, shapes customer-centric approaches, automates processes, and deciphers unstructured text. Several companies are using the sentiment analysis functionality to understand the voice of their customers, extract sentiments and emotions from text, and, in turn, derive actionable data from them. It helps capture the tone of customers when they post reviews and opinions on social media posts or company websites. Cdiscount, an online retailer of goods and services, uses semantic analysis to analyze and understand online customer reviews. When a user purchases an item on the ecommerce site, they can potentially give post-purchase feedback for their activity. This allows Cdiscount to focus on improving by studying consumer reviews and detecting their satisfaction or dissatisfaction with the company’s products.

An Overview of Conversational AI- Understanding Its Popularity

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. For us humans, there is nothing more simple than recognising the meaning of a sentence based on the punctuation or intonation used. 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? This is a key concern for NLP practitioners responsible for the ROI and accuracy of their NLP programs.

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. 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 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. 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. 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.

For example, ‘tea’ refers to a hot beverage, while it also evokes refreshment, alertness, and many other associations. On the other hand, collocations are two or more words that often go together. 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. In real application of the text mining process, the participation of domain experts can be crucial to its success.

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. QuestionPro, a survey and research platform, might have certain features or functionalities that could complement or support the semantic analysis process. Uber strategically analyzes user sentiments by closely monitoring social networks when rolling out new app versions. This practice, known as “social listening,” involves gauging user satisfaction or dissatisfaction through social media channels.

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. 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.

Efficiently working behind the scenes, semantic analysis excels in understanding language and inferring intentions, emotions, and context. 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 task, we try to detect the semantic relationships present in a text. Usually, relationships involve two or more entities such as names of people, places, company names, etc. In this component, we combined the individual words to provide meaning in sentences. Lexical analysis is based on smaller tokens but on the contrary, the semantic analysis focuses on larger chunks. Semantic analysis transforms data (written or verbal) into concrete action plans.

Moreover, while these are just a few areas where the analysis finds significant applications. Its potential reaches into numerous other domains where understanding language’s meaning and context is crucial. Search engines can provide more relevant results by understanding user queries better, considering the context and meaning rather than just keywords. As discussed earlier, semantic analysis is a vital component of any automated ticketing support.

• For instance, a direct word-to-word translation might result in grammatically correct sentences that sound unnatural or lose their original intent.
• It is also essential for automated processing and question-answer systems like chatbots.
• 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.
• Semiotics refers to what the word means and also the meaning it evokes or communicates.
• 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.
• Despite the good results achieved with a bag-of-words, this representation, based on independent words, cannot express word relationships, text syntax, or semantics.

In the fields of cultural studies and media studies, textual analysis is a key component of research. Researchers in these fields take media and cultural objects – for example, music videos, social media content, billboard advertising – and treat them as texts to be analyzed. It’s easier to see the merits if we specify a number of documents and topics.

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. This technology is already in use and is analysing the emotion and meaning of exchanges between humans and machines. Read on to find out more about this semantic analysis and its applications for customer service. Semantic Analysis is a subfield of Natural Language Processing (NLP) that attempts to understand the meaning of Natural Language.

Semantic networks is a network whose nodes are concepts that are linked by semantic relations. The most popular example is the WordNet [63], an electronic lexical database developed at the Princeton University. Depending on its usage, WordNet can also be seen as a thesaurus or a dictionary [64]. Jovanovic et al. [22] discuss the task of semantic tagging in their paper directed at IT practitioners. Semantic tagging can be seen as an expansion of named entity recognition task, in which the entities are identified, disambiguated, and linked to a real-world entity, normally using a ontology or knowledge base.

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. Each element is designated a grammatical role, and the whole structure is processed to cut down on any confusion caused by ambiguous words having multiple meanings. In this model, each document is represented by a vector whose dimensions correspond to features found in the corpus. When features are single words, the text representation is called bag-of-words. Despite the good results achieved with a bag-of-words, this representation, based on independent words, cannot express word relationships, text syntax, or semantics.

NeuraSense Inc, a leading content streaming platform in 2023, has integrated advanced semantic analysis algorithms to provide highly personalized content recommendations to its users. By analyzing user reviews, feedback, and comments, the platform understands individual user sentiments and preferences. 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. Thanks to tools like chatbots and dynamic FAQs, your customer service is supported in its day-to-day management of customer inquiries.

As technology continues to evolve, one can only anticipate even deeper integrations and innovative applications. 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. Semantic analysis analyzes the grammatical format of sentences, including the arrangement of words, phrases, and clauses, to determine relationships between independent terms in a specific context.

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. It makes the customer feel “listened to” without actually having to hire someone to listen. RStudio is the Integrated Development Environment (IDE) for working on R projects.

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. 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.

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. You can foun additiona information about ai customer service and artificial intelligence and NLP. After the selection phase, 1693 studies were accepted for the information extraction phase. In this phase, information about each study was extracted mainly based on the abstracts, although some information was extracted from the full text. The results of the accepted paper mapping are presented in the next section. The conduction of this systematic mapping followed the protocol presented in the last subsection and is illustrated in Fig.

In the ever-expanding era of textual information, it is important for organizations to draw insights from such data to fuel businesses. Semantic Analysis helps machines interpret the meaning of texts and extract useful information, thus providing invaluable data while reducing manual efforts. In Natural Language, the meaning of a word may vary as per its usage in sentences and the context of the text. 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. 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.