With the acceleration of urbanization and industrialization, regional development is becoming increasingly disordered. Thus, how to balance economic development and ecological protection is a key question. To comprehensively evaluate land development suitability, an evaluation index system for construction land and farmland suitability was established in this study based on natural and social-economic information. After selecting an island in China as the main case study in this work, the reference method was applied in grading and value assigning for all indices. In addition, the analytic hierarchy process and expert evaluation method were used to determine the index weighting. After scenario analysis was adopted in the study to analyze the impact of the policy orientation on the suitability evaluation, we finally evaluated the landscape ecological quality of different scenarios. Results showed the landscape ecological quality of a coordinated development scenario was the highest. The results indicated that coordinated development which takes the ecological protection and urban development into account may be a better choice for land planning.
With the spurt of online user-generated contents on web, sentiment analysis has become a very active research issue in data mining and natural language processing. As the most important indicator of sentiment, sentiment words which convey positive and negative polarity are quite instrumental for sentiment analysis. However, most of the existing methods for identifying polarity of sentiment words only consider the positive and negative polarity by the Cantor set, and no attention is paid to the fuzziness of the polarity intensity of sentiment words. In order to improve the performance, we propose a fuzzy computing model to identify the polarity of Chinese sentiment words in this paper. There are three major contributions in this paper. Firstly, we propose a method to compute polarity intensity of sentiment morphemes and sentiment words. Secondly, we construct a fuzzy sentiment classifier and propose two different methods to compute the parameter of the fuzzy classifier. Thirdly, we conduct extensive experiments on four sentiment words datasets and three review datasets, and the experimental results indicate that our model performs better than the state-of-the-art methods.
Review rating is more helpful than review binary classification for many decision processes such as consumption decision-making, company product quality tracking and public opinion mining. In the review rating, reviewers are influenced not only by their own subjective feelings, but also by others' rating to the same product. Existing review rating prediction methods are mainly based on the content of reviews, which only consider the subjective factors of reviewers, but not consider the impact of other people in the social relations of reviewers. Based on it, we propose a review rating prediction method by incorporating the character of reviewer's social relations, as regularization constraints, into content-based methods. In addition, we further propose a method to classify the social relations of reviewers into strong social relation and ordinary social relation. For strong social relation of reviewers, we give higher weight than ordinary social relation when incorporating the two social relations into content-based methods. Experiments on two real movie review datasets demonstrate that the method of considering different social relations has better performance than the content-based methods and the method of considering social relations as a whole.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.