How Semantic Analysis Impacts Natural Language Processing

However, there is a lack of studies that integrate the different branches of research performed to incorporate text semantics in the text mining process. Secondary studies, such as surveys and reviews, can integrate and organize the studies that were already developed and guide future works. It is the first part of semantic analysis, in which we study the meaning of individual words.

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 semantic text analysis approaches. Looking for the answer to this question, we conducted this systematic mapping based on 1693 studies, accepted among the 3984 studies identified in five digital libraries. In the previous subsections, we presented the mapping regarding to each secondary research question.

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

These resources can be used for enrichment of texts and for the development of language specific methods, based on natural language processing. Semantics gives a deeper understanding of the text in sources such as a blog post, comments in a forum, documents, group chat applications, chatbots, etc. With lexical semantics, the study of word meanings, semantic analysis provides a deeper understanding of unstructured text. 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.

• Powerful semantic-enhanced machine learning tools will deliver valuable insights that drive better decision-making and improve customer experience.
• With the help of meaning representation, we can link linguistic elements to non-linguistic elements.
• 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 is often accomplished by locating and extracting the key ideas and connections found in the text utilizing algorithms and AI approaches. 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. 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).

Personalization and Recommendation Systems:

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. 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. The advantage of a systematic literature review is that the protocol clearly specifies its bias, since the review process is well-defined.

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. 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 a similar kind. We observe that our approach performs the best for both the additional datasets, with the difference being less noticeable on the BBC dataset.

The main parts of the protocol that guided the systematic mapping study reported in this paper are presented in the following. As discussed in previous articles, NLP cannot decipher ambiguous words, which are words that can have more than one meaning in different contexts. Semantic analysis is key to contextualization that helps disambiguate language data so text-based NLP applications can be more accurate. 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. Efficiently working behind the scenes, semantic analysis excels in understanding language and inferring intentions, emotions, and context.

Specifically, we focus on DNN classifiers in conjunction with word embeddings for representing and feeding the input text to the predictive model. Readers might agree that there’s a clear distinction between the types of jokes each of the topics in Figure 4 represent. For more compelling examples of text analysis, see this post on predicting future stock prices, or read a recent publication using social media data (Kern et al., 2016). With all preliminary steps completed, you can create the DTM and carry out a PCA with it. However, depending on how large your DTM is (and they tend to be quite large), a PCA as I described above can take a long time to run and require lots of memory. Fortunately, JMP uses singular value decomposition in place of eigenvalue decomposition to make this analysis very efficient!

A probable reason is the difficulty inherent to an evaluation based on the user’s needs. In empirical research, researchers use to execute several experiments in order to evaluate proposed methods and algorithms, which would require the involvement of several users, therefore making the evaluation not feasible in practical ways. In addition to the text representation model, text semantics can also be incorporated to text mining process through the use of external knowledge sources, like semantic networks and ontologies, as discussed in the “External knowledge sources” section.

The semantic of the sentences get varied according to the textual context it is used. In natural language processing, determining the semantic likeness between sentences is an important research area. As a result, a lot of research is done in determining the semantic likeness in the text. Our proposed work utilizes Term Frequency-based Inverse Document Frequency model and Glove algorithm-based word embeddings vector for determining the semantic similarity among the terms in the textual contents. Lemmatizer is utilized to reduce the terms to the most possible smallest lemmas.

Beyond just understanding words, it deciphers complex customer inquiries, unraveling the intent behind user searches and guiding customer service teams towards more effective responses. Moreover, QuestionPro might connect with other specialized semantic analysis tools or NLP platforms, depending on its integrations or APIs. This integration could enhance the analysis by leveraging more advanced semantic processing capabilities from external tools. QuestionPro often includes text analytics features that perform sentiment analysis on open-ended survey responses. While not a full-fledged semantic analysis tool, it can help understand the general sentiment (positive, negative, neutral) expressed within the text.

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. 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.

• 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).
• Secondary studies, such as surveys and reviews, can integrate and organize the studies that were already developed and guide future works.
• The authors present an overview of relevant aspects in textual entailment, discussing four PASCAL Recognising Textual Entailment (RTE) Challenges.
• If you are looking for a dedicated solution using semantic analysis, contact us.

This implies that whenever Uber releases an update or introduces new features via a new app version, the mobility service provider keeps track of social networks to understand user reviews and feelings on the latest app release. Semantic Analysis is a subfield of Natural Language Processing (NLP) that attempts to understand the meaning of Natural Language. Understanding Natural Language might seem a straightforward process to us as humans.

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The first technique refers to text classification, while the second relates to text extractor. 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. For example, ‘tea’ refers to a hot beverage, while it also evokes refreshment, alertness, and many other associations.

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]. In this study, we identified the languages that were mentioned in paper abstracts.

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 lexical-only word2vec pre-trained embeddings outperform both sense-based approaches, out of which SensEmbed achieves the highest accuracy. Retrofitting word2vec vectors improves the classification results to a minor extent over the Ohsumed dataset. Given the subjective nature of the field, different methods used in semantic analytics depend on the domain of application. A systematic review is performed in order to answer a research question and must follow a defined protocol. The protocol is developed when planning the systematic review, and it is mainly composed by the research questions, the strategies and criteria for searching for primary studies, study selection, and data extraction.

Bos [31] indicates machine learning, knowledge resources, and scaling inference as topics that can have a big impact on computational semantics in the future. 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. However, as our goal was to develop a general mapping of a broad field, our study differs from the procedure suggested by Kitchenham and Charters [3] in two ways. Firstly, Kitchenham and Charters [3] state that the systematic review should be performed by two or more researchers.