The boom in wireless networking technology has led to an exponential increase in the number of web comments. Therefore, sentiment analysis of web comments is vital, and aspect-based sentiment analysis(ABSA) is very useful for the sentiment feature extraction of web comments. Currently, context-dependent sentiment feature typically derives from recurrent neural networks (RNN), and an average target vector usually replaces the target vector. However, web comments have become increasingly complex, and RNN may lose some essential sentiment information. At the same time, the average target vector may be the wrong target feature. We propose a new Transformer based memory network (TF-MN) to correct the shortcomings of the previous method. In TF-MN, the task becomes the question-answering process, which optimizes context, question, and the memory module. We use a global self-attention mechanism and a local attention mechanism (memory network) to construct emotionally inclined web comment semantics. Since self-attention can only obtain global semantic links, words such as nouns, prepositions, and adverbs still affect the emotional extraction of comments. To shield the influence of unrelated vocabulary on classification, we propose to use improved memory networks to optimize the extraction of web comments semantics. We conduct experiments on two datasets, and experimental results show that our model exceeds the state-of-the-art model.
Sustainability issues have gained growing awareness in recent years. Governments play an important role in environment and resources problems since they can affect enterprises’ production activities by enacting policies and regulations. To promote green production in the long term associated with the consideration of financial intervention of governments, we establish a three-population model of suppliers, manufacturers and governments based on evolutionary game theory, and analyze the evolutionary stable strategies (ESS) of their unilateral and joint behaviors. Further, system dynamics (SD) is applied to empirical analysis for exploring the dynamic interaction of the populations’ strategy, and the key factors affecting ESS are also discussed in detail. The results show that: (1) the proportion of green suppliers and manufacturers in their groups determines whether the government implements regulation; (2) any party of the supplier and manufacturer that adopts green strategy could promote green behavior of the other; (3) the government is advised to supervise and implement reward and punishment mechanism under the low proportion of green supply chain; (4) government regulation could promote the corporations to adopt green behavior and should preferentially implements the mechanism on manufacturers. The results provide insights into the policy-making of governments and enterprises management on sustainable development.
Selecting optimal suppliers in fuzzy environments has become a major challenge for enterprises. Reputation plays an important role in the process of supplier selection because of its fuzziness, dynamicity, and transitivity. In this study, we first present a novel intuitionistic fuzzy sets (IFS)-hyperlink-induced topic search (HITS) method that combines the intuitionistic fuzzy set with the hyperlink-induced topic search (HITS) algorithm to extend the ability of processing fuzzy information in order to obtain post-propagated reputation values of suppliers. Then, we employ the dynamic intuitionistic fuzzy weighted average operator to gain dynamic reputation values and other evaluation attribute values. After that, intuitionistic fuzzy entropy weight method is adopted to acquire more accurate weights for each evaluation attribute. Finally, we employ the Vlsekriterijumska Optimizacija I Kompromisno Resenje method to acquire comprehensive evaluation values of candidate supplier to select optimal suppliers. Two groups of experiments for supplier selection are given to explain feasibility and practicality of the proposed method.
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