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

The authors present a chronological analysis from 1999 to 2009 of directed probabilistic topic models, such as probabilistic latent semantic analysis, latent Dirichlet allocation, and their extensions. The “Method applied for systematic mapping” section presents an overview of systematic mapping method, since this is the type of literature review selected to develop this study and it is not widespread in the text mining community. 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.

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. According to IBM, semantic analysis has saved 50% of the company’s time on the information gathering process. 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. Wikipedia concepts, as well as their links and categories, are also useful for enriching text representation [74–77] or classifying documents [78–80]. Dagan et al. [26] introduce a special issue of the Journal of Natural Language Engineering on textual entailment recognition, which is a natural language task that aims to identify if a piece of text can be inferred from another.

Semantic kernels for text classification based on topological measures of feature similarity”

Thus, as we already expected, health care and life sciences was the most cited application domain among the literature accepted studies. This application domain is followed by the Web domain, what can be explained by the constant growth, in both quantity and coverage, of Web content. In this model, each document is represented by a vector whose dimensions correspond to features found in the corpus.

A general text mining process can be seen as a five-step process, as illustrated in Fig. The process starts with the specification of its objectives in the problem identification step. The text mining analyst, preferably working along with a domain expert, must delimit the text mining application scope, including the text collection that will be mined and how the result will be used. 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. 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.

IEEE Transactions on Knowledge and Data Engineering

The subscript of each word sense denotes POS information (e.g., nouns, in this example), while the superscript denotes a sense numeric identifier, differentiating between-individual word senses. Given a word sense, we can unambiguously identify the corresponding synset, enabling us to resolve potential ambiguity (Martin and Jurafsky Reference Martin and Jurafsky2009; Navigli Reference Navigli2009) of polysemous words. Thus, in the following paragraphs, we will use the notation

$l.p.i$

to refer to the synset that contains the i-th word sense of the lexicalization l that is of a POS p.

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. 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. These approaches utilize syntactic and lexical rules to get the noun phrases, terminologies and entities from documents and enhance the representation using these linguistic units. For example, Papka and Allan (1998) take advantage of multi-words to increase the efficiency of text retrieval systems.

Additionally, it delves into the contextual understanding and relationships between linguistic elements, enabling a deeper comprehension of textual content. Experiments over a US immigration dataset show that this approach outperforms supervised latent dirichlet allocation (LDA) (Mcauliffe and Blei Reference Mcauliffe and Blei2008) on document classification. In Li et al. (Reference Li, Wei, Yao, Chen and Li2017), the authors use a document-level embedding that is based on word2vec and concepts mined from knowledge bases. They evaluate their method on dataset splits from 20-Newsgroups and Reuters-21578, but this evaluation uses limited versions of the original datasets.

Given the candidate synsets retrieved from the NLTK WordNet API, the ones that are annotated with a POS tag that does not match the respective tag of the query word are discarded. Similarly to the 20-Newsgroups dataset case, we move on to the error analysis, with Figure 9(a) depicting the confusion matrix with the misclassified instances (i.e., diagonal entries are omitted). For better visualization, it illustrates only the 26 classes with at least 20 samples, due to the large number of classes in the Reuters dataset.

Applying semantic analysis in natural language processing can bring many benefits to your business, regardless of its size or industry. If you wonder if it is the right solution for you, this article may come in handy. In real application of the text mining process, the participation of domain experts can be crucial to its success.

• Semantic analysis significantly improves language understanding, enabling machines to process, analyze, and generate text with greater accuracy and context sensitivity.
• Automatic text classification is the task of organizing documents into pre-determined classes, generally using machine learning algorithms.
• With the help of semantic analysis, machine learning tools can recognize a ticket either as a “Payment issue” or a“Shipping problem”.
• A candidate word is mapped to its embedding representation and compared to the list of available synset vectors.
• Accordingly, an accurate text classifier should have the capability of using this semantic information.

We present the textual (raw text) component of our learning pipeline in Section 3.1, the semantic component in Section 3.2, and the training process that builds the classification model in Section 3.3. 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. Specifically, you have to determine what will constitute a “document.” A document can be a sentence, paragraph, article, or book, among many other choices. You do this by filtering out “stop words” (those extremely common words that aren’t likely to help unveil the structure in the text corpus, such as “the,” “and,” “from,” “is,” “of,” etc.). For example, the columns “analysis” and “analytics” in Figure 2 are redundant and can be combined into one column “analy.” This is called stemming.

Approaches to Meaning Representations

We observe that the misclassification occurrences are more frequent, but less intense than those in the 20-Newsgroup dataset. Additionally, Figure 9(b) depicts the label-wise performance of our best configuration. 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). In such cases, lack of annotator agreement occurs regularly and increases the expected discrimination difficulty of the dataset, as we discard neither superfluous labels nor multi-labeled instances.

Thanks to tools like chatbots and dynamic FAQs, your customer service is supported in its day-to-day management of customer inquiries. 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. B2B and B2C companies are not semantic text analysis 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. 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.

An Introduction to Natural Language Processing NLP

Subevent e2 also includes a negated has_location predicate to clarify that the Theme’s translocation away from the Initial Location is underway. A final has_location predicate indicates the Destination of the Theme at the end of the event. As mentioned earlier, not all of the thematic roles included in the representation are necessarily instantiated in the sentence. Through the analysis of our semantic similarity calculation data, this study finds that there are some differences in the absolute values of the results obtained by the three algorithms.

This includes making explicit any predicative opposition denoted by the verb. For example, simple transitions (achievements) encode either an intrinsic predicate opposition (die encodes going from ¬dead(e1, x) to dead(e2, x)), or a specified relational opposition (arrive encodes going from ¬loc_at(e1, x, y) to loc_at(e2, x, y)). Creation predicates and accomplishments generally also encode predicate oppositions.

NLP Libraries

This could mean, for example, finding out who is married to whom, that a person works for a specific company and so on. This problem can also be transformed into a classification problem and a machine learning model can be trained for every relationship type. Semantic Similarity is the task of determining how similar

two sentences are, in terms of what they mean.

However, we did find commonalities in smaller groups of these classes and could develop representations consistent with the structure we had established. Many of these classes had used unique predicates that applied to only one class. We attempted to replace these with combinations of predicates we had developed for nlp semantic other classes or to reuse these predicates in related classes we found. Other classes, such as Other Change of State-45.4, contain widely diverse member verbs (e.g., dry, gentrify, renew, whiten). A class’s semantic representations capture generalizations about the semantic behavior of the member verbs as a group.

Controversies Surrounding Natural Language Processing (NLP)

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 future prospects of semantic analysis domain and finally the study is concluded with the result section where areas of improvement are highlighted and the recommendations are made for the future research. This study also highlights the weakness and the limitations of the study in the discussion (Sect. 4) and results (Sect. 5).