Интеллектуальная поддержка формирования образовательных программ на основе нейросетевых моделей языка с учетом требований рынка труда

The active development of the digital economy today imposes high requirements on the adaptability, practical orientation and quality of educational content. Existing approaches to the intelligent decision support of the formation of educational programs based on ontological models, expert systems and heuristic algorithms do not allow effectively taking into account and tracking changes both in the labor market and in the open educational content space in the Massive Open Online Courses (MOOC). Instead, it is proposed to use approaches to the semantic analysis based on the well-known neural network language model word2vec, which is trained without supervision on large text corpora. The complexity of semantic analysis is to move the definition of semantic similarity measures for short texts of the extracted entities (course topics, learning outcomes, job requirements, etc.) to matching of large structured documents, such as professional standard, an educational program. To take into account the interrelations of entities, a graph model is introduced for representing the educational and professional domain. The paper proposes an artificial intelligent method of forming recommendations for the actualization of the learning outcomes and content of educational programs. At the first stage, the actual requirements of the labor market are determined based on a semantic matching of job requirements with the content of professional standards. The second stage includes a semantic matching of the content of academic disciplines with the requirements of the labor market. At the third stage, a semantic search of relevant educational content is carried out among the programs of disciplines of leading universities and massive open online courses (MOOC). During the fourth stage, final recommendations on updating the educational program are formed. The experiment demonstrated the possibility of applying the method for matching learning outcomes and content of disciplines with the requirements of professional standards and evaluation using the example of the educational program (bachelor degree) of computer science and engineering.Активное развитие отраслей цифровой экономики сегодня предъявляет высокие требования к адаптивности, практической направленности и качеству современных образовательных программ. Существующие подходы к интеллектуальной поддержке формирования образовательных программ на основе онтологических моделей, экспертных систем и эвристических алгоритмов не позволяют эффективно учитывать и отслеживать изменения как на рынке труда, так и в пространстве открытого образовательного контента в среде Интернет. Вместо этого предлагается использовать подходы к семантическому анализу текстов на основе известной нейросетевой модели языка word2vec, обучаемой без учителя на больших текстовых корпусах. Сложность сопоставительного семантического анализа заключается в переходе от определения меры семантической близости для отдельных коротких описаний анализируемых сущностей (тем курсов, результатов обучения, требований вакансий и т. д.) к сопоставлению больших структурируемых документов, таких как профессиональный стандарт, образовательная программа по направлению подготовки. Для учета взаимосвязей сущностей вводится графовая модель представления образовательной и профессиональной области. В работе предлагается интеллектуальный метод формирования рекомендаций по актуализации целей и содержания образовательных программ, включающий четыре этапа анализа. На первом этапе определяются актуальные требования рынка труда на основе семантического сопоставления фрагментов вакансий с содержанием профессиональных стандартов. Второй этап включает в себя семантическое сопоставление содержания учебных дисциплин с требованиями рынка труда. На третьем этапе производится семантический поиск релевантного образовательного контента среди программ дисциплин ведущих вузов и онлайн-курсов. В ходе четвертого этапа формируются итоговые рекомендации по актуализации образовательной программы. В рамках эксперимента продемонстрирована возможность применения метода для сопоставления результатов обучения и содержания дисциплин с требованиями профессиональных стандартов с оценкой качества на примере образовательной программы по направлению «Информатика и вычислительная техника»

Context Driven Bipolar Adjustment for Optimized Aspect Level Sentiment Analysis

122–127World Wide Web provides numerous opinionated data that can influence users. Reviews on online data highly affect the user’s perception while buying a particular or related product from an online shopping site. The online review provided by a customer helps other customers to make up their decision regarding purchasing that item. Looking at the developer’s and producer’s perspective, the opinions of customers on their manufactured items is helpful in identifying deformities as well as scope for improving its quality. Equipped with all this information, the product can be developed and managed more efficiently. Along with the overall rating of the product, the feature-based rating will have a great impact on the decision-making process of the customer. In this paper, an optimized scheme of aspect level sentiment analysis is presented to analyze the online reviews of a product. Reviews ratings have been used for learning approach. Inherently biased reviews are considered to optimize the Aspect Level Sentiment Analysis. Bi-polar aspect level sentiment analysis model has been trained using multiple kernels of support vector machine to optimize the results. Lexicon based aspect level sentiment analysis is performed first and later on the basis of bipolar words adjustment, and its effect on results, aspect level sentiment analysis for efficient optimization has been performed. A Web Crawler is developed to extract data from Amazon. The results obtained outperformed traditional lexicon based Aspect Level Sentiment Analysis

Word2Vec model for sentiment analysis of product reviews in Indonesian language

Online product reviews have become a source of greatly valuable information for consumers in making purchase decisions and producers to improve their product and marketing strategies. However, it becomes more and more difficult for people to understand and evaluate what the general opinion about a particular product in manual way since the number of reviews available increases. Hence, the automatic way is preferred. One of the most popular techniques is using machine learning approach such as Support Vector Machine (SVM). In this study, we explore the use of Word2Vec model as features in the SVM based sentiment analysis of product reviews in Indonesian language. The experiment result show that SVM can performs well on the sentiment classification task using any model used. However, the Word2vec model has the lowest accuracy (only 0.70), compared to other baseline method including Bag of Words model using Binary TF, Raw TF, and TF.IDF. This is because only small dataset used to train the Word2Vec model. Word2Vec need large examples to learn the word representation and place similar words into closer position