Aspect-Level Sentiment Analysis Based on Deep Learning

In recent years,deep learning methods have developed rapidly and found application in many fields,including natural language processing.In the field of aspect-level sentiment analysis,deep learning methods can also greatly improve the performance of models.However,previous studies did not take into account the relationship between user feature extraction and contextual terms.To address this issue,we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method.To be specific,we design user comment feature extraction(UCFE)to distill salient features from users’historical comments and transform them into representative user feature vectors.Then,the aspect-sentence graph convolutional neural network(ASGCN)is used to incorporate innovative techniques for calculating adjacency matrices;meanwhile,ASGCN emphasizes capturing nuanced semantics within relationships among aspect words and syntactic dependency types.Afterward,three embedding methods are devised to embed the user feature vector into the ASGCN model.The empirical validations verify the effectiveness of these models,consistently surpassing conventional benchmarks and reaffirming the indispensable role of deep learning in advancing sentiment analysis methodologies.

Quantifying Creep of Concrete Filled Steel Tubes

Using age adjusted effective modulus(AAEM)method,creep of concrete filled steel tube(CFST)member was formulated considering of creep coefficient and aging coefficient.Ten CFST specimens were tested including eight for creep and two for shrinkage.The experimental result was compared with the computed result using AAEM in which the creep coefficient was taken from calibration of ACI model based on experimental result on sealed concrete,and aging coefficient was supplied from relaxation test on sealed concrete specimen.Furthermore,the creep of CFST member was analyzed using author's own subroutine to input concrete properties through user programmable feature(UPF)in ANSYS software.Comparison was made on authors' own experimental database,some existing experimental results,and results from AAEM and numerical analysis.Finally,the conditions of applicability of AAEM method are put forward,and numerical approach to compute creep of CFST specimen is delineated.

Research on Semantic and Cache Model of Relational Database

The paper firstly analyze cache replacement strategies at present, and proposed the ideas of the semantic query cache replacement based on user access features, and describe the semantic similarity calculation and realize the algorithm of replacement strategy. The strategy use semantic to match information in the query cache, through dynamic analysis and tracking three characteristics of user access time, user access to content and Business Association, give out the similarity minimum of the cache item, to improve the hit ratio of the cache and the response time and throughput of the server is improved.

Sockpuppet gang detection on social media sites

Users of social media sites can use more than one account. These identities have pseudo anonymous properties, and as such some users abuse multiple accounts to perform undesirable actions, such as posting false or misleading re- marks comments that praise or defame the work of others. The detection of multiple user accounts that are controlled by an individual or organization is important. Herein, we define the problem as sockpuppet gang （SPG） detection. First, we analyze user sentiment orientation to topics based on emo- tional phrases extracted from their posted comments. Then we evaluate the similarity between sentiment orientations of user account pairs, and build a similar-orientation network （SON） where each vertex represents a user account on a so- cial media site. In an SON, an edge exists only if the two user accounts have similar sentiment orientations to most topics. The boundary between detected SPGs may be indistinct, thus by analyzing account posting behavior features we propose a multiple random walk method to iteratively remeasure the weight of each edge. Finally, we adopt multiple community detection algorithms to detect SPGs in the network. User ac- counts in the same SPG are considered to be controlled by the same individual or organization. In our experiments on real world datasets, our method shows better performance than other contemporary methods.