In a semantic network, information is represented as nodes connected by a set of marked directed lines that represent relationships between nodes (Kang et al., 2017). The assumption behind SNA is a specific connection between the words or concepts that frequently co-occur in the text, along with statistical indicators that can measure these connections. As a result, SNA enables the extraction of important ideas by recognizing emergent clusters of concepts rather than evaluating the frequency of solitary words (Kang et al., 2017; Golob et al., 2018). In this way, we can enhance our understanding of the text of social media users’ comments. Wimalasuriya and Dou , Bharathi and Venkatesan , and Reshadat and Feizi-Derakhshi  consider the use of external knowledge sources (e.g., ontology or thesaurus) in the text mining process, each one dealing with a specific task. Wimalasuriya and Dou  present a detailed literature review of ontology-based information extraction.
One valuable technique tied to these processes is known as natural language processing (NLP), which is a field of artificial intelligence (AI) that provides the ability to read, understand and derive meaning from human languages. NLP is used in many different types of data analytics processes, including the following. Although several researches have been developed in the text mining field, the processing of text semantics remains an open research problem. The field lacks secondary studies in areas that has a high number of primary studies, such as feature enrichment for a better text representation in the vector space model. We found considerable differences in numbers of studies among different languages, since 71.4% of the identified studies deal with English and Chinese. Thus, there is a lack of studies dealing with texts written in other languages.
9, in which the font size reflects the frequency of the methods and algorithms among the accepted papers. We can note that the most common approach deals with latent semantics through Latent Semantic Indexing (LSI) [2, 120], a method that can be used for data dimension reduction and that is also known as latent semantic analysis. The Latent Semantic Index low-dimensional space is also called semantic space.
It dissects the response text into syntax and semantics to accurately perform text analysis. Like other tools, Lexalytics also visualizes the data results in a presentable way for easier analysis. Features: Uses NLP (Natural Language Processing) to analyze text and give it an emotional score.
This fact is not unexpected, since life sciences have a long time concern about standardization of vocabularies and taxonomies. Among the most common problems treated through the use of text mining in the health care and life science is the information retrieval from publications of the field. The search engine PubMed  and the MEDLINE database are the main text sources among these studies. There are also studies related to the extraction of events, genes, proteins and their associations [34–36], detection of adverse drug reaction , and the extraction of cause-effect and disease-treatment relations [38–40]. 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].
Since the co-occurrence probability of two concepts may be zero, one is introduced to avoid errors. An image belonging to a visual concept with a small semantic gap should have a relatively similar visual appearance. Some previous works regarded the responses of SVM detectors as the measurement of the semantic gap  and constructed the similarity matrix of visual features to quantify the visibility of tags .
We are aware that our work is just one of many steps in visual emotion recognition task. In the future, how to predict the concept labels of images based on the discovered affective semantic concepts and combine them with deep learning networks will be a possible extension. Additionally, this concept-based visual emotion can be introduced to aesthetics analysis as well. The proposed SALOM is assessed using four experiments to discover the effectiveness of semantics in the opinion mining process.
It is also an essential component of data science, which involves the collection, analysis, and interpretation of large datasets. In this article, we will explore how semantics and data science intersect, and how semantic analysis can be used to extract meaningful insights from complex datasets. 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.
This discipline is also called NLP or “natural language processing”. As such, when a customer contacts customer services, a text analysis is performed and the role of semantic analysis is to detect all the subjective elements in an exchange: approach, positive feeling, dissatisfaction, impatience, etc.
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. The results of the systematic mapping, as well as identified future trends, are presented in the “Results and discussion” section. Text mining techniques have become essential for supporting knowledge discovery as the volume and variety of digital text documents have increased, either in social networks and the Web or inside organizations. Although there is not a consensual definition established among the different research communities , text mining can be seen as a set of methods used to analyze unstructured data and discover patterns that were unknown beforehand .
To further exploit these selected affective concepts, we train linear classifiers to get concept scores for each learned affective semantic concept. Then, we develop a visual emotion classification framework that exploits the affective semantic metadialog.com concepts scores as the intermediate representation for emotion analysis. Regarding to the mentioned disadvantages in Tables 1 and and2,2, the previous lexicon-based studies tried to improve the accuracy of reviews classification.
Hence, it is critical to identify which meaning suits the word depending on its usage. Organizations keep fighting each other to retain the relevance of their brand. There is no other option than to secure a comprehensive engagement with your customers. Businesses can win their target customers’ hearts only if they can match their expectations with the most relevant solutions.
As I promised in the introduction, now I will show how this model will provide additional valuable information that supervised models are not providing. Namely, I will show that this model can give us an understanding of the sentiment complexity of the text. In addition to the fact that both scores are normally distributed, their values correlate with the review’s length. A simple explanation is that one can potentially express more positive or negative emotions with more words. Of course, the scores cannot be more than 1, and they saturate eventually (around 0.35 here). Please note that I reversed the sign of NSS values to better depict this for both PSS and NSS.
For example, “the thief” is a noun phrase, “robbed the apartment” is a verb phrase and when put together the two phrases form a sentence, which is marked one level higher. Other semantic phenomena contribute to the complexity of automatic sentiment analysis. It is often a source of error for analysts to find opposition between two propositions, linked by just or yet. This simplification of use by Internet users make analysis all the more difficult since the “sentences” are not constructed in the same way and do not follow the same rules. As long as the uses of the language are continuously evolving, it would be too complex to recognize a large number of syntactic forms for any sentence structure to be analyzed. Other approaches include analysis of verbs in order to identify relations on textual data [134–138].
Semantic methods involve assigning truth values to the premises and conclusion until we find one in which all premises are TRUE and the conclusion is FALSE. In SENTENTIAL LOGIC our main semantic method is constructing a truth table (short or long).