Semantic Analysis Research Articles

Evaluating the Effectiveness of the LexRank and LSA Algorithm in Automatic Text Summarization for Indonesian Language

The aim of this study is to evaluate how effective the Lexrank algorithm and Latent semantic analysis (LSA) are in automatic text summarization for the Indonesian language. This research focuses on natural language processing and handling of excessive data. We applied both algorithms to generate text summaries using the INDOSUM dataset, which contains about 20,000 news articles in Indonesian with manual summaries. To assess performance, the ROUGE metric was used, which includes aspects of precision, recall, and F1 score. In all tested metrics, LSA outperformed Lexrank. LSA had a precision of 0.57, recall of 0.67, and an F1 score of 0.59, whereas Lexrank had a precision of 0.46, recall of 0.52, and an F1 score of 0.48. These result indicate that LSA is better at gathering important information from the original text than Lexrank.

Raylike Subject Model of Text Deauthorization in Media Genres (in English and Belarusian)

The purpose of the article is to construct a raylike subject model of text deauthorization based on the material of English and Belarusian media texts of information genres with the identification of common and distinctive features. In the result of analysis of the ways in which the subject components of deauthorization structures are located in news articles and interviews using the modeling method, methods of semantic and contextual analysis, the raylike subject model of text deauthorization is constructed, which exhibits similar and distinctive features in the compared English and Belarusian media texts. The characteristics of the analyzed model that are common to the compared media cultures are established: structural and semantic parallelism of deauthorization structures, the presence of similar narrating subject groups, the dichotomy of opinions of specialists and non-specialists. Different features of the analyzed model in the compared media cultures are identified, which relate to the nature of the dominant pragmatic marking and the formal-referential characteristics of the subject components in deauthorization structures. The genre specificity of the studied model in the interview texts is revealed, where a new version of raylike subject model is discovered, in which two collective opinions are contrasted on the principle of «we» vs «strangers», while a number of fortifying discursive signals are used to strengthen the we-position.

Determinants of Multilevel Discourse Outcomes in Anomia Treatment for Aphasia.

Individuals with aphasia identify discourse-level communication (i.e., language in use) as a high priority for treatment. The central premise of most aphasia treatments is that restoring language at the phoneme, word, and/or sentence level will generalize to discourse. However, treatment-related changes in discourse-level communication are modest, are poorly understood, and vary greatly among individuals with aphasia. In response, this study consisted of a multilevel discourse analysis of archival, monologic discourse outcomes across two high-intensity Semantic Feature Analysis (SFA) clinical trials. Aim 1 evaluated changes in theoretically motivated discourse outcomes representing lexical-semantic processing, lexical diversity, grammatical complexity, and discourse informativeness. Aim 2 explored the potential moderating role of nonlanguage cognitive factors (semantic memory, divided attention, and executive function) on discourse outcomes. This study was a retrospective analysis of archival monologic discourse outcomes after intensive SFA for n = 60 (Aim 1) and a subset n = 44 (Aim 2). Outcome measures included lexical-semantic processing (% semantic errors), lexical diversity (moving average type-token ratio), grammatical complexity (mean utterance length), and discourse informativeness (% correct information units). Bayesian generalized mixed-effects models were used to examine changes across four study time points: enrollment, entry, exit, and 1-month follow-up. The present study found no evidence for meaningful or statistically reliable improvements in monologue discourse performance after SFA when measured using standard, general-topic discourse stimuli. There was weak and inconsistent evidence that nonlanguage cognitive factors may play a role in moderating treatment response. These findings indicate a clear need to pair theoretically informed treatments designed to facilitate generalization to discourse with intentional measurement paradigms designed to capture it. Furthermore, there is a clear need to examine how established treatments, restorative or compensatory, can better facilitate generalization to discourse-level communication. These priorities are critical for meaningfully improving everyday communication and reducing the profound communication and psychosocial consequences of aphasia. https://doi.org/10.23641/asha.26524081.

Identify Issues That Influence Customer Satisfaction Using Sentiment Analysis and Topic Modeling

The development of internet users in Indonesia continues to increase over time, this makes customers comfortable with digital transactions. Trinusa Travelindo Company or better known as Traveloka, which is engaged in the online travel agent sector, has proven its extraordinary achievement, namely becoming the E-tourism application that is ranked 1st most visited by the Indonesian people in 2022. However, these results are also directly proportional to the number of negative reviews created in the application review itself. This study aims to identify what issues affect customer satisfaction with topic modelling. This study uses the text mining method derived from the results of Traveloka application user reviews. The data source obtained in this study is secondary data by using the Traveloka application review data crawling technique with samples from January to May. Based on sentiment analysis, positive sentiment is more dominant (50.38%) compared to negative sentiment (49.62%). User reviews are grouped based on the results of sentiment analysis, then Topic Modelling is carried out to find out what issues affect customer satisfaction. The topic modelling of the data used is Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) with the help of phyton 6.3. to find out what words and topics often appear or as factors that influence customer satisfaction. The most influential negative issues include the refund and payment process, pay later services, and the ordering experience which is considered complicated. On the other hand, issues that contribute positively to customer satisfaction include product quality, service quality, and promos and discounts. This is reinforced by the results of topic modelling which show that these aspects are often the main concerns in user reviews on the Google Play store. Thus, this study suggests that in order to increase customer satisfaction, Traveloka needs to focus on improving the quality of service, products, and offering more attractive promos, while simplifying the refund process and ordering experience in the application, some still say it is complicated.

The lived experience of gambling-related harm in natural language.

Gambling-related harms can have a significant negative impact on disordered gamblers, lower risk gamblers, and affected others. Yet, most disordered and lower risk gamblers will never seek formal treatment, often due to the stigma and shame surrounding gambling. Online self-help forums are a popular alternative way for gamblers to anonymously seek help from others. Analysis of these interactions can provide a deeper understanding of gambling than more commonly used research methodologies. In the present study, we leverage recent developments in natural language processing to analyze posts on a U.K.-based online self-help gambling forum. Using correlated topic modeling, we canvass the various types of discussions among forum members. We also combine this approach with semantic similarity analysis based on sentence embeddings, to map first the posts, and then the 10 topics, onto six previously established gambling-related harm domains. The topic modeling revealed a cluster of discussions of many negative emotions, a topic regarding the positive emotions underlying the potential for change, a distinct topic regarding gambling's relationship harms, and numerous environmental factors that contributed to harm. Emotional/psychological and health harms were most strongly associated with users' posts, illustrating the multidimensionality of severe gambling-related harm. Our results reveal the co-occurrence of different harms, such as the frequent mentions of financial harms and concomitant emotional/psychological harms. The analysis of the lived experiences of gambling-related harm in natural language represents a useful tool for gambling research and can provide a different perspective to inform policy. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

One Country, Two Perspectives: A Corpus-Assisted Critical Discourse Analysis of Newspaper Coverage of the Greater Bay Area Scheme in Hong Kong and Mainland China

Despite extensive research on media framing of Hong Kong affairs, there remains a significant gap in the comparative analysis of media representations between Chinese and Hong Kong news discourses amid Hong Kong’s increasing integration with mainland China. This study employs a corpus-assisted critical discourse analysis to examine the discursive construction around the Greater Bay Area (GBA) scheme in the news discourses of South China Morning Post (SCMP) and China Daily (CD). The key emotion semantic analysis reveals that the SCMP predominantly projects negative emotions related to worry, while CD foregrounds positive emotions such as bravery. A word sketch of Hong Kong suggests that SCMP prefersd dynamic verbs highlighting the potential effects of the scheme, whereas CD emphasizes the actions associated with administration and regulation. The study contributes to an understanding of media shaping public perception, with real-world policy implications for cross-border communication strategies.

Supplemental Material for You Sound Like an Evil Young Man: A Distributional Semantic Analysis of Systematic Form-Meaning Associations for Polarity, Gender, and Age in Fictional Characters’ Names

Supplemental Material for You Sound Like an Evil Young Man: A Distributional Semantic Analysis of Systematic Form-Meaning Associations for Polarity, Gender, and Age in Fictional Characters’ Names

Analisis Puisi “pada suatu Hari Nanti” Karya Sapardi Djoko Damono: Kajian terhadap Jenis Makna dalam Ilmu Semantik

The use of many figurative words makes it difficult for readers to understand the meaning contained in the beautiful stanzas of poetry. The purpose of the research is to analyze semantically with the object of study of a poem entitled "Pada Satu Hari Nanti" written by Sapardi Djoko Damono. This research is studied through semantic analysis to examine the meaning that focuses on analyzing lexical meaning, grammatical meaning, referential meaning, and connotative / figurative meaning. In accordance with the above objectives, the type of research is descriptive qualitative research. Data collection is done using documentation techniques, library techniques, and note-taking techniques. The results of the analysis show that in each stanza of the poem there is a complexity of lexical, referential, grammatical, and connotative/figurative meanings that provide depth and richness of meaning in poetry as well as broader knowledge of the use of language in literary works and how meaning can be interpreted through semantic analysis. The use of many figurative words makes it difficult for readers to understand the meaning contained in the beautiful stanzas of poetry. The purpose of the research is to analyze semantically with the object of study of a poem entitled "Pada Satu Hari Nanti" written by Sapardi Djoko Damono. This research is studied through semantic analysis to examine the meaning that focuses on analyzing lexical meaning, grammatical meaning, referential meaning, and connotative / figurative meaning. In accordance with the above objectives, the type of research is descriptive qualitative research. Data collection is done using documentation techniques, library techniques, and note-taking techniques. The results of the analysis show that in each stanza of the poem there is a complexity of lexical, referential, grammatical, and connotative/figurative meanings that provide depth and richness of meaning in poetry as well as broader knowledge of the use of language in literary works and how meaning can be interpreted through semantic analysis.

Grounded semantics and principle-based analysis for incomplete argumentation frameworks

Incomplete Argumentation Frameworks (IAFs) enrich classical abstract argumentation with arguments and attacks whose actual existence is questionable. The usual reasoning approaches rely on the notion of completion, i.e. standard AFs representing “possible worlds” compatible with the uncertain information encoded in the IAF. Recently, extension-based semantics for IAFs that do not rely on the notion of completion have been defined, using instead new versions of conflict-freeness and defense that take into account the (certain or uncertain) nature of arguments and attacks. In this paper, we give new insights on both the “completion-based” and the “direct” reasoning approaches. First, we adapt the well-known grounded semantics to this framework in two different versions that do not rely on completions. After determining that our new semantics are polynomially computable, we provide a principle-based analysis of these semantics, as well as the “direct” semantics previously defined in the literature, namely the complete, preferred and stable semantics. Finally, we also provide new results regarding the satisfaction of principles by the classical “completion-based” semantics.

A Semantic Analysis of the User-Generated Content from Social Network Sites of Cashbuild Limited: Application of Text Mining, Machine Learning and Big Data Analytics toward the Development of a Customer-Centric Marketing Strategy

Social Network Sites (SNS) have grown rapidly over the past 15 years, which is in line with the growth of smartmobile phones. A key phenomenon of SNSs is User-Generated Content (UGC). It enables social engagements between users and their followers. UGC shares various perspectives on news, entertainment, and fun, as well as their perspectives on brands like Customer Experience (CX) and Customer Satisfaction (CS). UGC stimulates the Brand Attitudes (BA) of the readers if the user is trusted and credible. BAs influence the Purchase Intentions (PI) of the readers of UGC. Marketers like that UGC influences PI. They learn about CX and CS, which users share on the UGC. This semantic study uses Text Mining (TM) and Big Data Analysis (BDA) to extract keywords from the UGC of a building materials retail company, Cashbuild Limited (CBL) from the Republic of South Africa (RSA). The UGC was analysed using Power BI (PBI) to identify its sources and sentiments. The majority of the UGC in this study was obtained from Facebook (94.51%), followed by X.com (3.95%). The rest of the UGC was obtained from Blogs (0.79%) and Instagram (0.76%). 65.63% of the UGC was neutral, 29.74% positive, and 4.46% negative. UGC influences PI by mediating hedonic and utilitarian Brand Equity. The generation of a high-quality UGC was encouraged by CS - an outcome of CX. CX comes from the touchpoints across the CJ. Marketers want to ensure a good customer experience (CX) to achieve customer satisfaction (CS). Ultimately, CS influences future customer expectations (CE) when shared through UGC. 84 top keywords were extracted out of 16 328 hit sentences (HS) from the UGC of CBL using the MELTWATER program. The keywords were ranked to 51 frequency positions. CONCOR analysis was used to determine the connectivity and centrality of the keywords using Freeman’s coefficient and the Eigenvector value for each keyword. The results of the CONCOR analysis were used to identify 41 significant keywords based on their higher connectivity and centrality. The 41 significant keywords were matched with the 16 328 HSs. 14 keywords that matched less than 100 HSs were excluded. The remaining 27 keywords were subjected to exploratory factor analysis EFA on Statistical Program for Social Sciences (SPSS). 21 keywords were loaded on 8 factors, and the model explained 68.363% of the variance with a Kaizer Meyer Olkin (KMO) of 0.736 above 0.6. The 8 categories were compared to input from a panel of industry experts. 7 categories proposed by experts matched 7 out of 8 categories from the statistical model. 7 key themes proposed to be used in the study to develop Customer-centric Marketing Strategies (CCMS) in the building materials retail industry (BMRI) are DIY Kings, House Proud, Project Master, Renovators, Interior Image, Building Materials Inventory, and Promotions. All 5 objectives of the study were met. The study adds value to the development of CCMS learning from the UGC.

Development of public health decision-making based on semantic evaluation of EHR data

The study examines the way semantic analysis of data from electronic health records (EHRs) is performed. Implementing machine learning (ML) is challenging, EHRs are essential for acquiring medical data because they include textual information from physicians about patients' ailments and treatment plans, supporting well-informed public health decision-making. The goal of the study was to allow computers to semantically comprehend assessments, physical parts, indicators, and therapies in addition to implicitly identifying medical terms in EHRs. To efficiently identify medical phrases in electronic health records, a novel cubic support vector machine with a dipper-throated optimization (CSVM-DTO) strategy is suggested. This study uses tokenization as a pre-processing step for the data gathered from public sources. The semantic data of medical terminology is then extracted using the word-2-vector technique. Next, the named entities are found using the CSVM, and their efficiency is improved by implementing the DTO technique. Based on the experimental results, we can conclude that our suggested approach outperformed other current approaches in locating the identified entities inside the EHRs.

Role of Natural Language Processing in Document Understanding and Semantic Analysis: A Chinese Perspective

With the recent advancements in Natural Language Processing (NLP), there is a growing need to enhance the effectiveness and accuracy of document understanding and sentiment analysis particularly for Chinese text as it presents unique challenges of linguistics. To conduct this study, a hybrid approach was implemented which combined CNN and BiLSTM models with an ensemble voting mechanism for sentiment analysis on Chinese text. This method attained an accuracy of 97% and outperformed other techniques such as Text_CNN and AttentionBiLSTM with significant improvements in F1 score and recall. Results demonstrated a superior performance achieving 97% accuracy, along with a 95% F1 score and 97% recall. The present study extends the growing body of literature by underscoring the effectiveness of integrating BiLSTM and CNN models in sentiment analysis within the context of Chinese linguistics. It showcased enhanced document comprehension and capabilities of semantic analysis. Practically, this study provides a robust framework for leveraging BiLSTM and CNN models in the sentiment analysis of real-world. It offers significant boost in adequacy and reliability for processing Chinese text. The research limitations and future research indications have also been addressed in the study

Inheriting the glory of PāṆinian grammar into computer science

the integration of Pāṇinian grammar principles into computer science represents a significant advancement in computational linguistics and natural language processing. Pāṇini, an ancient Indian grammarian, formulated an elaborate system of grammar known as Aṣṭādhyāyī around the 4th century BCE, which has been revered for its systematic and concise rules governing Sanskrit language structure. In recent years, there has been a growing interest in adapting Pāṇinian principles to enhance computational frameworks for understanding human languages. This paper explores the theoretical foundations of Pāṇinian grammar and its applicability in the context of modern computational linguistics. The integration of Paninian grammar into computer science enhances the accuracy and efficiency of natural language processing system and contributes to preservation and dissemination of ancient linguistic knowledge. Through this the researchers aim to bridge gap between traditional linguistic scholarship and modern computational methodologies. By embracing the systematic and rigorous framework laid out by Pāṇini millennia ago, researchers are poised to achieve new breakthroughs in understanding and processing human language through innovative computational techniques. The journey from ancient Sanskrit grammar to innovative computer science exemplifies a harmonious blend of tradition and innovation in the quest for linguistic knowledge and technological advancement. Keywords - Pāṇini, Aṣṭādhyāyī, Computational linguistics, Machine translation, semantic analysis, syntactic rules.

Exploring global technogenic earthquakes from a scientometric prospective: research trends, semantic analysis, and research drivers

Exploring global technogenic earthquakes from a scientometric prospective: research trends, semantic analysis, and research drivers

Investigation of Exercise Interventions on Postoperative Recovery in Lung Cancer Patients: A Qualitative Study Using Web Crawling Technology.

Rapid recovery after lung cancer surgery is challenging. Exercise is a low-cost, effective method to expedite recovery. Despite numerous exercise interventions, many fail to consider patient perspectives, leading to low adherence and short-term effects. Understanding lung cancer patients' perspectives on postoperative exercise and exploring their exercise-related concerns and needs are crucial for enhancing the effectiveness of exercise-based rehabilitation programs. This study aims to analyze lung cancer patients' perspectives on postoperative exercise in their daily lives, exploring their concerns and needs related to postoperative exercise to help healthcare professionals develop personalized exercise plans. An internet crawling technique collected online inquiries from Baidu webpages about postoperative physical activity in lung cancer patients, using "lung cancer", "surgery", and"exercise" as keywords. The data was encoded, categorized, and analyzed using a large-scale semantic analysis platform in natural language processing and information retrieval to examine term frequency, sentiment tendencies, and attributes in the inquiry texts. Initially, 2727 queries were retrieved; after screening, deduplication, and cleansing, 201 unique queries were identified. Queries related to "modes of exercise" constituted the largest proportion. The most frequently occurring words in the word frequency analysis were "lung", "cancer", "should", "can", "long", "early", and "surgery", "exercise", "respiratory". Postoperative lung cancer patients demonstrate significant interest in whether they should engage in exercise, as well as in the appropriate types and duration of such activities, indicating a strong need for detailed guidance and knowledge related to exercise. The sentiment analysis showed a positive score of 87.5% and a negative score of 12.5%, indicating that postoperative lung cancer patients view exercise positively and have an enthusiastic attitude towards it. Among the positive sentiment attributes, "good" was the most frequently mentioned term, whereas "bad" and "surprising" were the most prevalent terms within the negative sentiment attributes. Postoperative physical activity receives limited attention from lung cancer patients, who emphasize their preferences for exercise modalities. Their inquiries often reflect psychological concerns, such as fear and helplessness caused by symptoms. Understanding patients' perspectives on postoperative physical activity within their real-life contexts can help integrate psychological support into exercise plans. This integration could guide healthcare professionals in developing more personalized postoperative exercise regimens for lung cancer patients.

Optimizing feature extraction for tampering image detection using deep learning approaches

Tamper image detection approach using deep learning involves, creating a model that can accurately identify and localize instances of image tampering, by employing advanced feature extraction methods, object detection algorithms, and optimization techniques that could be manipulated on need basis. Enhance the integrity of visual content by automating the detection of unauthorized alterations, to ensure the reliability of digital images across various applications and domains. The problem addressing the optimization feature extraction techniques involves the detection of subtle manipulations, handling diverse tampering techniques, and achieving robust performance across different types of images and scenarios. The proliferation of sophisticated image editing tools makes it challenging to detect tampered regions within images, necessitating proposed techniques for automated tamper image detection. The research work will focus on four different feature extraction algorithms such as non-negative factorization (NNF), singular value decomposition (SVD), explicit semantic analysis (ESA), principal component analysis (PCA), which are outsourced. Detecting tampered images through deep learning necessitates the meaningful selection and adjustment of several parameters to enhance the model's effectiveness. Integrating the feature extraction algorithm with the suggested methods effectively identifies critical features within the dataset, thereby improving the detection capabilities and achieving higher accuracy.

Medical device similarity analysis: a promising approach to medical device equivalence regulation

ABSTRACT Background This study aims to facilitate the identification of similar devices for both, the European Medical Device Regulation (MDR) and the US 510(k) equivalence pathway by leveraging existing data. Both are related to the regulatory pathway of read across for chemicals, where toxicological data from a known substance is transferred to one under investigation, as they aim to streamline the accreditation process for new devices and chemicals. Research design and methods This study employs latent semantic analysis to generate similarity values, harnessing the US Food and Drug Administration 510k-database, utilizing their ‘Device Descriptions’ and ‘Intended Use’ statements. Results For the representative inhaler cluster, similarity values up to 0.999 were generated for devices within a 510(k)-predicate tree, whereas values up to 0.124 were gathered for devices outside this group. Conclusion Traditionally, MDR equivalence involves manual review of many devices, which is laborious. However, our results suggest that the automated calculation of similarity coefficients streamlines this process, thus reducing regulatory effort, which can be beneficial for patients needing medical devices. Although this study is focused on the European perspective, it can find application within 510(k) equivalence regulation. The conceptual approach is reminiscent of chemical fingerprint similarity analysis employed in read-across.

Threat in the context of identifying the victim

It is indicated that an important feature of the injured person, which allows to distinguish him from other persons, is related to the infliction of damage. The etymological and semantic analysis of the term «victim» shows that damage is an integral feature of a victim. The article examines the possibility and expediency of recognizing a person as a victim in the criminal law field in the event of a threat to cause harm to him. Provisions of the current Criminal Code of Ukraine, the project of the new Criminal Code of Ukraine, national judicial practice are taken into account. It is argued that in context of the injured person, term damage is understood in two meanings: 1) narrowly (includes only real damage); 2) in a broad sense (includes both damage and the danger/threat of causing it, as well as the threat). At the same time, attributing the threat as an independent action to a type of harm, as suggested by authors of the draft Criminal Code of Ukraine, seems debatable, because in such a case the term «threat» will denote both an action and a consequence. It is also worth paying attention to whether there is a need to use both the term «threat» and the term «danger» in the draft Criminal Code of Ukraine. It has been established that the term «threat» in the context of the definition of the victim means both actual moral or other types of harm to him, as well as the threat/danger of his task. It has been proven that victim in a certain composition of a criminal offense can be characterized not only by harm in its narrow sense, but also by threat or danger of its occurrence, that is, harm in the broad sense, as well as the threat of harm. The threat in the context of the criminal-legal understanding of victim is important, first of all, if there is no implementation. In case of the implementation of a threat, which is provided as an independent crime and is a stage of a single criminal activity, united by a single intention and aimed at causing damage to the same object, qualification under the article providing for responsibility for the threat is not required, and therefore and there is no need to establish the victim of the threat. If a threat is a way of committing an act, then the victim is determined in the context of act in general, taking into account the threat as a way of committing it.

The birth of the Chinese character ‘奈’: Reflections on semantic differentiation

In this study, ‘奈’, is the earliest character in Ancient Chinese to derive its meaning from a single character. It was derived and developed from the sounds and characters corresponding to ‘奈’, ‘柰’ and ‘祟’. Thus, the formation and elimination of sounds in Ancient Chinese can be viewed through the one-sided development of Chinese characters from the time of the oracle bone script period to the present, but it can be seen that sound components are not all components of lexical variation, and thatW the process of character differentiation is ultimately influenced by the similarity of characters, their meaning and their development. The article describes a study to examine the development of lexical meaning and the relationship between its semantic elements: first, the original meaning of the characters and the extended meaning; second, the development of sound and dialect from the Ancient Chinese period to the Middle Chinese period; and third, the grammatical implementation and function that can be checked through it, and the semantic analysis elements found in the Chinese characters, excavated texts and traditional texts.

Psycho-semantic analysis of job advertisements as a communicative tool of educational marketing

Introduction. The relevance of the study of job advertisements for teachers and psychologists is associated with the need to find tools for regulating communication within the modern educational environment with dynamic, constantly changing working conditions. The publication of employer requirements sets the direction for changing the criteria for the success of the specialist, which changes the entire educational environment. The purpose of the study: to identify the categorical semantic structure of job advertisements for teachers (using the example of the "Infrastructure of Scientific Research Data" platform). Materials and methods. The object of the study was a corpus of teacher job vacancies from the "Infrastructure of Scientific Research Data" platform, published in 2023 in 83 regions of Russia (5561 vacancies). The job postings included descriptions of candidate requirements, information about the offered salary, and the region in which the vacancy was available. As a comparison group, a corpus of psychologist job vacancies (2076 vacancies) was considered. The following methods were used in the study: 1) Latent Dirichlet Allocation (LDA) method for identifying and analyzing the structure of requirements presented in teacher and psychologist job vacancies; 2) Term Frequency-Inverse Document Frequency (TF-IDF) method for categorical semantic analysis of the structure of teacher and psychologist job vacancies and description of the semantic framework (frame) of the job description; 3) Descriptive statistical methods for analyzing the needs of professionals in helping professions in different regions of Russia and the conditions offered to them. The results of the study. The regions of the Russian Federation with the greatest need for teachers and psychologists have been identified. Based on the statistical significance in the collection of text documents, the most demanded skills of teachers by employers are identified: methodological development and design of an individual educational route (0.167), work with children with disabilities (0.144), organization of the educational process (0.089). For vacancies of psychologists, the most significant were: work on investigative actions (0.319), methodical work in an educational organization (0.187), psychological and pedagogical support for successful development and training (0.129). The main meanings governing professional pedagogical activity are more focused on meta-skills based on the values of inclusion and continuous improvement of education, as well as on flexible skills (Soft skills) that increase competitiveness in various fields of pedagogical activity. In the vacancies of psychologists, professional skills (Hard skills) are relevant, applicable in specific areas, which somewhat narrows the scope of professional implementation. Conclusion. The results of the empirical study allow for considering job vacancy advertisements as one of the communicative tools of educational marketing. Analyzing the significant professional skills from the employer's perspective will enable the development of recommendations for the development of professional communication for teachers, which can be considered in the system of continuous professional education, as well as in regulating interaction within the educational environment. Automated analysis of job vacancies can become a successful interactive diagnostic tool for educational marketing, necessary for the development of the educational space and the improvement of the competitiveness of educational institutions.