Therefore it is a natural language processing problem where text needs to be understood in order to predict the underlying intent. The sentiment is mostly categorized semantic analysis of text into positive, negative and neutral categories. Cognitive and physiological terminologies reflect quantum-theoretic concepts (bold) in parallel way.

Same phenomena can be described in information terms such that action potentials are considered as signals linking binary neural registers while total activity of the nervous system is referred to as psyche, cognition or mind51,52. In traditional psychology, activity of the mind is described verbally as dynamics of ideas, thoughts, motives, emotions, etc.36,53. In physical terms, control of the living system’s behavior is understood as electrochemical process occurring in an individual’s nervous system including \(\sim \)100 billion neuron cells interacting with each other via action potentials47. After initial formation by receptor cells, action potentials are transmitted through multilevel neuronal chains to the central nervous system and the brain where their transformation is observed by variety of physical means48,49,50. Resulting electrochemical excitations are transferred to the organism’s behavioral facilities by descending neural pathways.

With the use of sentiment analysis, for example, we may want to predict a customer’s opinion and attitude about a product based on a review they wrote. Sentiment analysis is widely applied to reviews, surveys, documents and much more. Let’s look at some of the most popular techniques used in natural language processing.

When combined with machine learning, semantic analysis allows you to delve into your customer data by enabling machines to extract meaning from unstructured text at scale and in real time. Sentiment analysis plays a crucial role in understanding the sentiment or opinion expressed in text data. It is a powerful application of semantic analysis that allows us to gauge the overall sentiment of a given piece of text. In this section, we will explore how sentiment analysis can be effectively performed using the TextBlob library in Python. By leveraging TextBlob’s intuitive interface and powerful sentiment analysis capabilities, we can gain valuable insights into the sentiment of textual content.

These resources can be used for enrichment of texts and for the development of language specific methods, based on natural language processing. When the field of interest is broad and the objective is to have an overview of what is being developed in the research field, it is recommended to apply a particular type of systematic review named systematic mapping study [3, 4]. Systematic mapping studies follow an well-defined protocol as in any systematic review. The main differences between a traditional systematic review and a systematic mapping are their breadth and depth.

Stavrianou et al. [15] also present the relation between ontologies and text mining. Ontologies can be used as background knowledge in a text mining process, and the text mining techniques can be used to generate and update ontologies. 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 lower number of studies in the year 2016 can be assigned to the fact that the last searches were conducted in February 2016.

Only the broader context of document is used with some type of term frequency information in the vector space. Consequently, semantics of words that can be inferred from the finer context of its location in a sentence and its relations with neighboring words are usually ignored. However, meaning of words, semantic connections between words, documents and even classes are obviously important since methods that capture semantics generally reach better classification performances.

Semantic-enhanced machine learning tools are vital natural language processing components that boost decision-making and improve the overall customer experience. 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.

NLP-powered apps can check for spelling errors, highlight unnecessary or misapplied grammar and even suggest simpler ways to organize sentences. Natural language processing can also translate text into other languages, aiding students in learning a new language. Keeping the advantages of natural language processing in mind, let’s explore how different industries are applying this technology. Syntax is the grammatical structure of the text, whereas semantics is the meaning being conveyed. A sentence that is syntactically correct, however, is not always semantically correct. For example, “cows flow supremely” is grammatically valid (subject — verb — adverb) but it doesn’t make any sense.