Quantum semantics of text perception Scientific Reports
Nevertheless, we believe that our limitations do not have a crucial impact on the results, since our study has a broad coverage. This mapping shows that there is a lack of studies considering languages other than English or Chinese. The low number of studies considering other languages suggests that there is a need for construction or expansion of language-specific resources (as discussed in “External knowledge sources” section).
In this way, quantum approach allows to consider simple units of cognition while circumventing detailed description of the human’s mind and brain. At this level of modeling, numerous intricacies of human cognition are hidden, but continue to affect observable behavior (cf.76). Further sections illustrate this modeling approach semantic analysis of text on the process of subjective text perception. Such measure is provided by quantum theory where the required contextual probability calculus is based on the notion of quantum state21,22,23,24,25. This allows to account for contextual cognitive and behavioral phenomena by simple and quantitative models reviewed in15,26,27.
Word Sense Disambiguation
In contrast to existing surveys, this survey strives to concentrate and address all the above-mentioned deficiencies by presenting a focused and deeply detailed literature review on the application of semantic text classification algorithms. Cognitive states formed in the process of perception of text are fully compatible with quantum theoretic analysis methods. In this way, concurrence measure of quantum entanglement is imported from quantum theory to the cognitive domain for free.
The overall results of the study were that semantics is paramount in processing natural languages and aid in machine learning. 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). The results of the systematic mapping study is presented in the following subsections.
Better mixing via deep representations
It allows computers to understand and interpret sentences, paragraphs, or whole documents, by analyzing their grammatical structure, and identifying relationships between individual words in a particular context. Gathering market intelligence becomes much easier with natural language processing, which can analyze online reviews, social media posts and web forums. Compiling this data can help marketing teams understand what consumers care about and how they perceive a business’ brand.
With the availability of NLP libraries and tools, performing sentiment analysis has become more accessible and efficient. As we have seen in this article, Python provides powerful libraries and techniques that enable us to perform sentiment analysis effectively. By leveraging these tools, we can extract valuable insights from text data and make data-driven decisions. Overall, sentiment analysis is a valuable technique in the field of natural language processing and has numerous applications in various domains, including marketing, customer service, brand management, and public opinion analysis.