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

We observe that it varies significantly, with classes like earn and acq achieving excellent performance, while others perform rather poorly, for example, soybean and rice. Given that we are only interested in single-label classification, we treat the dataset as a single-labeled corpus using all sample and label combinations that are available in the dataset. This results into a noisy labeling that is typical among folksonomy-based annotation (Peters and Stock Reference Peters and Stock2007).

Semantic analysis aids search engines in comprehending user queries more effectively, consequently retrieving more relevant results by considering the meaning of words, phrases, and context. Semantic analysis allows for a deeper understanding of user preferences, enabling personalized recommendations in e-commerce, content curation, and more. Chatbots, virtual assistants, and recommendation systems benefit from semantic analysis by providing more accurate and context-aware responses, thus significantly improving user satisfaction. 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.

A new method for attribute extraction with application on text classification

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. 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.

In Elberrichi, Rahmoun, and Bentaalah (Reference Elberrichi, Rahmoun and Bentaalah2008), the bag-of-words vector representation (Salton and Buckley Reference Salton and Buckley1988) is combined with the WordNet semantic graph. A variety of semantic selection and combination strategies are explored, along with a supervised feature selection phase that is based on the chi-squared statistic. The experimental evaluation on the 20-Newsgroups and Reuters datasets shows that the semantic augmentation aids classification, especially when considering the most frequent related concept of a word. Frequency-based approaches are examined in Nezreg, Lehbab, and Belbachir (Reference Nezreg, Lehbab and Belbachir2014) over the same two datasets, applying multiple classifiers to terms, WordNet concepts and their combination.

The Journal of Machine Learning Research

For our purposes, we’ll use Rasa to build a chatbot that handles inquiries on these topics. Yet, for all the recent advances, there is still significant room for improvement. In this article, we’ll show how a customer assistant chatbot can be extended to handle a much broader range of inquiries by attaching it to a semantic search backend.

Only the 300-dimensional pre-trained word2vec surpasses the “embedding-only” baseline. Surprisingly enough, retrofitting the embeddings consistently results in inferior performance, both for the pre-trained ones and for those fitted from scratch. Regarding sense embeddings, both supersenses and SensEmbed vectors work well, surpassing the “embedding-only” baseline, but they do not outperform our approach.

Additionally, it delves into the contextual understanding and relationships between linguistic elements, enabling a deeper comprehension of textual content. Text mining studies steadily gain importance in recent years due to the wide range of sources that produce enormous amounts of data, such as social networks, blogs/forums, web sites, e-mails, and online libraries publishing research papers. The growth of electronic textual data will no doubt continue to increase with new developments in technology such as speech to text engines and digital assistants or intelligent personal assistants. Automatically processing, organizing and handling this textual data is a fundamental problem.

Text mining has several important applications like classification (i.e., supervised, unsupervised and semi-supervised classification), document filtering, summarization, and sentiment analysis/opinion classification. Natural Language Processing (NLP), Machine Learning (ML) and Data Mining (DM) methods work together to detect patterns from the different types of the documents and classify them in an automatic manner (Sebastiani, 2005). 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.

These facts can justify that English was mentioned in only 45.0% of the considered studies. 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 text semantic analysis 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. Also, in the theme of automatic building of ontologies from texts, Cimiano et al. [21] argue that automatically learned ontologies might not meet the demands of many possible applications, although they can already benefit several text mining tasks.

The combined approach yields the best results for both datasets; however, (a) it uses handcrafted features for the representation of textual information and (b) it employs shallow methods for classification, and (c) it considers subsets of the two datasets. Early attempts produce shallow vector space features to represent text elements, such as words and documents, via histogram-based methods (Katz Reference Katz1987; Salton and Buckley Reference Salton and Buckley1988; Joachims Reference Joachims1998). In these cases, latent topics are inferred to form a new, efficient representation space for text. Regarding neural approaches, a neural language model applied on word sequences is used in Bengio et al. (Reference Bengio, Ducharme, Vincent and Jauvin2003) to jointly learn word embeddings and the probability function of the input word collection. Deep neural models are used to learn semantically aware embeddings between words (Mikolov et al.

Reference Mikolov, Karafiát, Burget, Cernocký and Khudanpur2010; Reference Mikolov, Kombrink, Burget, Cernocký and Khudanpur2011). These embeddings try to maintain semantic relatedness between concepts, but also support meaningful algebraic operators between them.

But due to leaps in the performance of NLP systems made after the introduction of transformers in 2017, combined with the open source nature of many of these models, the landscape is quickly changing. Companies like Rasa have made it easy for organizations to build sophisticated agents that not only work better than their earlier counterparts, but cost a fraction of the time and money to develop, and don’t require experts to design. Create eye catching word clouds using important topics and words from your transcripts and text. Now, when it comes to text analysis vs text analytics  the main difference is that text analysis refers to qualitative results while text analytics is the process of identifying trends and stats – aka quantitative results from your text data. In the dynamic landscape of customer service, staying ahead of the curve is not just a… In the early days of semantic analytics, obtaining a large enough reliable knowledge bases was difficult.

Likewise, the word ‘rock’ may mean ‘a stone‘ or ‘a genre of music‘ – hence, the accurate meaning of the word is highly dependent upon its context and usage in the text. Hence, under Compositional Semantics Analysis, we try to understand how combinations of individual words form the meaning of the text. For this reason, it’s good practice to include multiple annotators, and to track the level of agreement between them.

Word Senses

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. 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. 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. In-Text Classification, our aim is to label the text according to the insights we intend to gain from the textual data.

In Vulic and Mrkšic (Reference Vulic and Mrkšic2018), embeddings are fine-tuned to respect the WordNet hypernymy hierarchy, and a novel asymmetric similarity measure is proposed for comparing such representations. This results in state-of-the-art performance on multiple lexical entailment tasks. 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.

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. Today, machine learning algorithms and NLP (natural language processing) technologies are the motors of semantic analysis tools. IBM’s Watson provides a conversation service that uses semantic analysis (natural language understanding) and deep learning to derive meaning from unstructured data. It analyzes text to reveal the type of sentiment, emotion, data category, and the relation between words based on the semantic role of the keywords used in the text.

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. 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.

• It is also essential for automated processing and question-answer systems like chatbots.
• A systematic review is performed in order to answer a research question and must follow a defined protocol.
• 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.
• 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.
• As we discussed, the most important task of semantic analysis is to find the proper meaning of the sentence.

Additionally, we utilize the Reuters-21578Footnote h dataset, which contains news articles that appeared on the Reuters financial newswire in 1987 and are commonly used for text classification evaluation. Using the traditional “ModApte” variant, the corpus comprises 9584 and 3744 training and test documents, respectively, with a labelset of 90 classes. The latter corresponds to categories related to financial activities, ranging from consumer products and goods (e.g., grain, oilseed, palladium) to more abstract monetary topics (e.g., money-fx, gnp, interest).