Feature-Level Sentiment Analysis Based on Rules and Fine-Grained Domain Ontology

Wei, Wie; Liu, Yiping; Wei, Leiru

doi:10.5771/0943-7444-2020-2-105

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Psychological education meets the needs of talent cultivation in higher education and provides a new vision for theoretical research of ideological and political education in colleges and universities. ere is a content crossover between psychological education and ideological and political education, which is in line with the law of ideological and political work in colleges and universities and helps to further innovate the theoretical research of ideological and political education in colleges and universities [1]. In this study, to be able to obtain ideal optimization results, a two-layer parallel algorithm is proposed in two architectural modes of single-computer multicore and networked multicomputer, research is carried out on how to improve the computational e ciency, and a model system for college students' mental health education is constructed through a multiobjective matrix regular optimization algorithm.…”

Section: Introductionmentioning

confidence: 72%

Construction of a Mental Health Education Model for College Students Based on Fine-Grained Parallel Computing Programming

Zhu

Xue

2022

Mathematical Problems in Engineering

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Mental health and mental health problems of college students are becoming more and more obvious, and there is more and more negative news caused by psychological problems, and society from all walks of life has given high attention to this problem. Given the new situations and new problems, how to keep up with the times and reform and innovate in the content, method, and path of psychological education in colleges and universities is an important work of ideological and political education in colleges and universities. Because fine-grained category information can provide rich semantic clues, fine-grained parallel computing techniques are widely used in tasks such as sensitive feature filtering, medical image classification, and dangerous goods detection. In this study, we adopt a fine-grained parallel computing programming approach and propose a multiobjective matrix regular optimization algorithm that can simultaneously perform the joint square root, low-rank, and sparse regular optimization for bilinear visual features, which is used to stabilize the higher-order semantic information in bilinear features, improve the generalization ability of features, and apply it to the construction of mental health education models for college students to promote the construction of mental health education bases, improve mental health education network platform, and strengthen the construction of mental health education data platform. A new practical aspect has been added to the abstract. The saliency-guided data augmentation method in this study is an improvement on random data augmentation but reduces the randomness in the data augmentation process and significantly improves the results. The best result belongs to SCutMix data augmentation, which improves by 1.9% compared to the baseline network.

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Section: Introductionmentioning

confidence: 72%

Construction of a Mental Health Education Model for College Students Based on Fine-Grained Parallel Computing Programming

Zhu

Xue

2022

Mathematical Problems in Engineering

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“…e PSO algorithm consists of four processes: initialization of the population, calculation of the fitness of each particle, updating of the individual global optimal solution, and updating of the velocity V and position X of the particles, where the population initialization process is equivalent to an iterative calculation of the initialization results as input to the subsequent processes [15].…”

Section: Enterprise Critical Link Digital Transmissionmentioning

confidence: 99%

Construction of College Students’ Mental Health Education Model Based on Data Analysis

Tang

2022

Scientific Programming

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This paper presents an in-depth study and analysis of the model of college students’ mental health education using fine-grained parallel computational programming. In the experimental group, the total level of positive thoughts and the level of life satisfaction significantly increased and the level of depression significantly decreased before and after the implementation of the intervention; in the control group, the level of life satisfaction significantly increased before and after the implementation of the intervention, and there was no significant difference in the total level of positive thoughts and the level of depression. Based on the above results, the positive thinking group-assisted training in this study was effective in improving the level of positive thinking and life satisfaction and reducing the level of depression into two categories of high school students: those who were susceptible and those who were symptomatic but satisfied, thus improving their mental health status, and may provide operational references for future intervention studies. At the same time, some studies have pointed out that when college students are faced with stressful situations, they will have higher psychological levels if they take a positive way to deal with them, and when college students are under stress, they will use a positive psychological restraint mechanism to cope. From a macroperspective, the research results can also be used to guide the training system of mental health education teachers, teachers’ professional development, and career development planning, showing certain application value.

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“…Generally speaking, coarse-grained analysis includes text-level and sentence-level sentiment analysis, while fine-grained analysis involves word-level sentiment analysis. The fine-grained text sentiment analysis extracts the polarity and tendency of emotion from several aspects through the analysis of words to obtain an accurate degree of sentiment tendency (Wei et al , 2020). At present, fine-grained sentiment analysis based on a sentiment dictionary is a common method for text sentiment analysis (Song et al , 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring destination image through online reviews: an augmented mining model using latent Dirichlet allocation combined with probabilistic hesitant fuzzy algorithm

Luo

Tong

Zhang

et al. 2021

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PurposeIn the era of information overload, the density of tourism information and the increasingly sophisticated information needs of consumers have created information confusion for tourists and scenic-area managers. The study aims to help scenic-area managers determine the strengths and weaknesses in the development process of scenic areas and to solve the practical problem of tourists' difficulty in quickly and accurately obtaining the destination image of a scenic area and finding a scenic area that meets their needs.Design/methodology/approachThe study uses a variety of machine learning methods, namely, the latent Dirichlet allocation (LDA) theme extraction model, term frequency-inverse document frequency (TF-IDF) weighting method and sentiment analysis. This work also incorporates probabilistic hesitant fuzzy algorithm (PHFA) in multi-attribute decision-making to form an enhanced tourism destination image mining and analysis model based on visitor expression information. The model is intended to help managers and visitors identify the strengths and weaknesses in the development of scenic areas. Jiuzhaigou is used as an example for empirical analysis.FindingsIn the study, a complete model for the mining analysis of tourism destination image was constructed, and 24,222 online reviews on Jiuzhaigou, China were analyzed in text. The results revealed a total of 10 attributes and 100 attribute elements. From the identified attributes, three negative attributes were identified, namely, crowdedness, tourism cost and accommodation environment. The study provides suggestions for tourists to select attractions and offers recommendations and improvement measures for Jiuzhaigou in terms of crowd control and post-disaster reconstruction.Originality/valuePrevious research in this area has used small sample data for qualitative analysis. Thus, the current study fills this gap in the literature by proposing a machine learning method that incorporates PHFA through the combination of the ideas of management and multi-attribute decision theory. In addition, the study considers visitors' emotions and thematic preferences from the perspective of their expressed information, based on which the tourism destination image is analyzed. Optimization strategies are provided to help managers of scenic spots in their decision-making.

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Feature-Level Sentiment Analysis Based on Rules and Fine-Grained Domain Ontology

Cited by 6 publications

References 18 publications

Construction of a Mental Health Education Model for College Students Based on Fine-Grained Parallel Computing Programming

Construction of a Mental Health Education Model for College Students Based on Fine-Grained Parallel Computing Programming

Construction of College Students’ Mental Health Education Model Based on Data Analysis

Exploring destination image through online reviews: an augmented mining model using latent Dirichlet allocation combined with probabilistic hesitant fuzzy algorithm

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