Recently, aspect-level sentiment analysis methods using graph convolutional network (GCN)-based structures with fairly good performance have been introduced. However, previous GCN-based methods often experience one of the following limitations. First, GCNs usually use edges with binary weights. However, binary weights are not helpful in many tasks. Second, these GCNs only focus on extracting node features from some single words or phrases and ignore their context in the entire sentence or paragraph or only consider the information of independent phrases when determining the relation between two graph edges overlooking the semantic relation among these phrases. Finally, no studies simultaneously use the information on the context, the semantic relation, and the sentiment knowledge among words or phrases to build GCNs for aspect-level sentiment analysis. Therefore, to resolve these limitations, in this study, we propose a new method, the CANN-SSCG model, as follows. First, we built three separate heterogeneous graphs, namely, syntax-based, semantic-based, and context-based graphs. Second, we constructed a general heterogeneous graph (SSC graph) by combining the three constructed graphs. We then converted the nodes of the SSC graph into sentence vectors using a GCN with two layers (creating an SSC-GCN). Finally, we used a convolutional neural network algorithm with attention to position embeddings (CANN) on the output of the SSC-GCN model for aspect-level sentiment analysis. The experiments, which used three different datasets, including reviews and tweets, showed that the proposed method yields promising results based on the F1score.

Social networks are a very popular channel for people to communicate with, to find, to reference other users before making decisions, especially those concerning purchase. How can users’ opinions within social networks be used in making decisions cost-effective and reliable? In this paper, we propose an approach for supporting decision-making based on measuring the user satisfaction level by analyzing the sentiment of aspects and mining the fuzzy decision trees. Our proposal has been proved to overcome some of the disadvantages of previous methods. Specifically, we consider the fuzzy sentiments of users for aspects and the effects of user satisfaction, dissatisfaction, and hesitation for decision-making. The proposed method comprises four main stages. The first stage identifies a topic, which the user is interested. In the second stage, aspects of the topic and their sentiments within tweets are extracted. At the third stage, the user satisfaction level is calculated according to each kind of sentiment identified in the second step. Finally, a decision matrix is constructed, and the fuzzy decision tree is built to generate a set of rules for supporting users in decision-making. The experiments using tweets show that the proposed method achieves promising results regarding the accuracy and gained information.

In recommendation systems, rating is an important user activity reflecting their opinions. Once the users return their rates about the items suggested from the systems, the user rates can be used to adjust recommendation process. However, users can make some mistakes (e.g., nature noises) during rating the items. As the recommendation systems receive more incorrect rates, the performance of such systems might be decreased. To solve the problem, in this paper, we focus on an interactive recommendation system which can help users to correct their own rates.

A MANET comprises a set of mobile devices which are capable of communicating with others. Despite its flexibility and portability, it suffers from energy limitation. If the energy consumption of the nodes in the MANET cannot be balanced efficiently, some nodes will die in an early stage due to the energy depletion, which in turn partitions the whole network and leads to the communication failure. To balance the energy consumption, we propose the dynamic packet balancing agent for MANETs. The agent utilizes the HELLO packets to update energy information along a path, which enables the dynamic update of the energy information of each path, and sends packets via the path with the best energy condition. The agent is further applied to AOMDV, a well-known multipath protocol. Simulation result generated by NS2 (network simulator version 2) shows that our proposal outperforms AOMDV in balancing energy consumption.