Text mining and semantics: a systematic mapping study Journal of the Brazilian Computer Society Full Text

With lexical semantics, the study of word meanings, semantic analysis provides a deeper understanding of unstructured text. Consequently, in order to improve text mining results, many text mining researches claim that their solutions treat or consider text semantics in some way. However, text mining is a wide research field and there is a lack of secondary studies that summarize and integrate the different approaches.

Advances in NLP have led to breakthrough innovations such as chatbots, automated content creators, summarizers, and sentiment analyzers. The field’s ultimate goal is to ensure that computers understand and process language as well as humans. In simple words, we can say that lexical semantics represents the relationship between lexical items, the meaning of sentences, and the syntax of the sentence. 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?

How does Semantic Text Analysis differ from Syntactic Analysis?

As previously stated, the objective of this systematic mapping is to provide a general overview of semantics-concerned text mining studies. The papers considered in this systematic mapping study, as well as the mapping results, are limited by the applied search expression and the research questions. 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.

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. 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. Semantic analysis allows organizations to interpret the meaning of the text and extract critical information from unstructured data.

That leads us to the need for something better and more sophisticated, i.e., Semantic Analysis. If a text contains an appropriate number of relevant key phrases, search engines will evaluate it positively. Watery articles and ones with not enough keywords will not show on the first page of search results. Texts overstuffed with keywords are treated as spam, and search engines rarely show them. Whether it is Siri, Alexa, or Google, they can all understand human language (mostly).

As we look towards the future, it’s evident that the growth of these disciplines will redefine how we interact with and leverage the vast quantities of data at our disposal. To learn more and launch your own customer self-service project, get in touch with our experts today. RStudio is the Integrated Development Environment (IDE) for working on R projects.

Although our mapping study was planned by two researchers, the study selection and the information extraction phases were conducted by only one due to the resource constraints. In this process, the other researchers reviewed the execution of each systematic mapping phase and their results. Secondly, systematic reviews usually are done based on primary studies only, nevertheless we have also accepted secondary studies (reviews or surveys) as we want an overview of all publications related to the theme. Text classification and text clustering, as basic text mining tasks, are frequently applied in semantics-concerned text mining researches.

Cdiscount’s semantic analysis of customer reviews

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. 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.

• As we look towards the future, it’s evident that the growth of these disciplines will redefine how we interact with and leverage the vast quantities of data at our disposal.
• 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.
• 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.

They allow for the extraction of patterns, trends, and important information that would otherwise remain hidden within unstructured text. This process is fundamental in making sense of the ever-expanding digital textual universe we navigate daily. The significance of a word or phrase can vary dramatically depending on situational elements such as culture, location, or even the specific domain of knowledge it pertains to. Semantic Analysis uses context as a lens, sharpening the focus on what is truly being conveyed in the text.

In that case, it becomes an example of a homonym, as the meanings are unrelated to each other. Semantic analysis, on the other hand, is crucial to achieving a high level of accuracy when analyzing text. Google’s Hummingbird algorithm, made in 2013, makes search results more relevant by looking at what people are looking for.

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]. Text mining initiatives can get some advantage by using external sources of knowledge. Thesauruses, taxonomies, ontologies, and semantic networks are knowledge sources that are commonly used by the text mining community. Semantic networks is a network whose nodes are concepts that are linked by semantic relations.

The analysis can segregate tickets based on their content, such as map data-related issues, and deliver them to the respective teams to handle. The platform allows Uber to streamline and optimize the map data triggering the ticket. 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. Apart from these vital elements, the semantic analysis also uses semiotics and collocations to understand and interpret language.

Text summarization extracts words, phrases, and sentences to form a text summary that can be more easily consumed. The accuracy of the summary depends on a machine’s ability to understand language data. 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. 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.

Understanding Natural Language Processing (NLP)

The analysis of the semantic core of a text assesses its keyword density, water and spamming. I will show you how straightforward it is to conduct Chi square test based feature selection on our large scale data set. This process is experimental and the keywords may be updated as the learning algorithm improves. For Example, you could analyze the keywords in a bunch of tweets that have been categorized as “negative” and detect which words or topics are mentioned most often.

• These systems will not just understand but also anticipate user needs, enabling personalized experiences that were once unthinkable.
• Following this, the relationship between words in a sentence is examined to provide clear understanding of the context.
• This process is experimental and the keywords may be updated as the learning algorithm improves.

Wikipedia concepts, as well as their links and categories, are also useful for enriching text representation [74–77] or classifying documents [78–80]. Earlier, tools such as Google translate were suitable for word-to-word translations. However, with the advancement of natural language processing and deep learning, translator tools can determine a user’s intent and the meaning of input words, sentences, and 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.

Textual analysis in this context is usually creative and qualitative in its approach. Researchers seek to illuminate something about the underlying politics or social context of the cultural object they’re investigating. The service highlights redundant vocabulary used to magnify the meaning and words and phrases containing no specific information, recommending the highlighted units for deletion or replacement.

Why Prioritizing Human Element is Crucial for Smart Manufacturing

You can foun additiona information about ai customer service and artificial intelligence and NLP. 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 lower number of studies in the year 2016 can be assigned to the fact that the last searches were conducted in February 2016. Semantic analysis helps fine-tune the search engine optimization (SEO) strategy by allowing companies to analyze and decode users’ searches. The approach helps deliver optimized and suitable content to the users, thereby boosting traffic and improving result relevance.

This proficiency goes beyond comprehension; it drives data analysis, guides customer feedback strategies, shapes customer-centric approaches, automates processes, and deciphers unstructured text. 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. 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.

To know the meaning of Orange in a sentence, we need to know the words around it. The bottom of the page shows the number of symbols, with and without stop-words, and the density of particular units. The keyword density is shown in a separate field, indicating their ratio to the entire text and the relevant phrases. Search engines will evaluate such texts as poor in quality and not show them on the first page.

These systems will not just understand but also anticipate user needs, enabling personalized experiences that were once unthinkable. To navigate these complexities, your understanding of the landscape of semantic analysis must include an appreciation for its nuances and an awareness of its limitations. Engaging with the ongoing progress in this discipline will better equip you to leverage semantic insights, mindful of their inherent subtleties and the advances still on the horizon.

Languages

Thus, the low number of annotated data or linguistic resources can be a bottleneck when working with another language. The second most frequent identified application domain is the mining of web texts, comprising web pages, blogs, reviews, web forums, social medias, and email filtering [41–46]. The high interest in getting some knowledge from web texts can be justified by the large amount and diversity of text available and by the difficulty found in manual analysis. Nowadays, any person can create content in the web, either to share his/her opinion about some product or service or to report something that is taking place in his/her neighborhood. Companies, organizations, and researchers are aware of this fact, so they are increasingly interested in using this information in their favor.

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. This convergence of Semantic IoT heralds a new age of smart environments, where decision-making is data-driven and context-aware. It ensures a level of precision and personalization in automated systems, ultimately leading to enhanced efficiency, comfort, and safety within our daily lives. In today’s data-driven world, the ability to interpret complex textual information has become invaluable. Semantic Text Analysis presents a variety of practical applications that are reshaping industries and academic pursuits alike.

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.

Search algorithms now prioritize understanding the intrinsic intent behind user queries, delivering more accurate and contextually relevant results. By doing so, they significantly reduce the time users spend sifting through irrelevant information, thereby streamlining the search process. The intricacies of human language mean that texts often contain a level of ambiguity and subtle nuance that machines find difficult to decipher. A single sentence may carry multiple meanings or rely on cultural contexts and unwritten connotations to convey its true intent. Strides in semantic technology have begun to address these issues, yet capturing the full spectrum of human communication remains an ongoing quest.

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. 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.

Syntax analysis and Semantic analysis can give the same output for simple use cases (eg. parsing). However, for more complex use cases (e.g. Q&A Bot), Semantic analysis gives much better results. It makes the customer feel “listened to” without actually having to hire someone to listen. A certain amount of irrelevant content is natural, but it should be reduced to the minimum to produce a water-free high-quality text. The number of keywords and their ratio to the total text and to its core will show at the bottom of the page. For example, the top 5 most useful feature selected by Chi-square test are “not”, “disappointed”, “very disappointed”, “not buy” and “worst”.

The search engine PubMed [33] and the MEDLINE database are the main text sources among these studies. There are also studies related to the extraction of events, genes, proteins and their associations [34–36], detection of adverse drug reaction [37], and the extraction of cause-effect and disease-treatment relations [38–40]. Text mining techniques have become essential for supporting knowledge discovery as the volume and variety of digital text documents have increased, either in social networks and the Web or inside organizations. 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.

From enhancing business intelligence to advancing academic research, semantic analysis lays the groundwork for a future where data is not just numbers and text, but a mirror reflecting the depths of human thought and expression. Sentiment Analysis is a critical method used to decode the emotional tone behind words in a text. By analyzing customer reviews or social media commentary, businesses can gauge public opinion about their services or products. This understanding allows companies to tailor their strategies to meet customer expectations and improve their overall experience.

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. 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.

As we discussed, the most important task of semantic analysis is to find the proper meaning of the sentence. Understanding these terms is crucial to NLP programs that seek to draw insight from textual information, extract information and provide data. It is also essential for automated processing and question-answer systems like chatbots. It may offer functionalities to extract keywords or themes from textual responses, thereby aiding in understanding the primary semantic text analysis topics or concepts discussed within the provided text. Chatbots, virtual assistants, and recommendation systems benefit from semantic analysis by providing more accurate and context-aware responses, thus significantly improving user satisfaction. Future NLP is envisioned to transcend current capabilities, allowing for seamless interactions between humans and AI, significantly boosting the efficacy of virtual assistants, chatbots, and translation services.