Aspect Sentiment Triplet Extraction (ASTE) is the task to extract aspects, opinions and associated sentiments from sentences. Previous studies do not adequately consider the complicated interactions between aspect and opinion terms in both extraction logic and strategy. We present a novel Double Policy Network with Multi-Tag based Reward model (DPN-MTR), which adopts two networks ATE, TSOTE and a Trigger Mechanism to execute ASTE task following a more logical framework. A Multi-Tag based reward is also proposed to solve the limitations of existing studies for identifying aspect/opinion terms with multiple tokens (one term may consist of two or more tokens) to a certain extent. Extensive experiments are conducted on four widely-used benchmark datasets, and demonstrate the effectiveness of our model in generally improving the performance on ASTE significantly.

Smart Parking schemes cannot succeed without the engagement and support of the drivers who may benefit from their use. This study investigates engagement with a Smart Parking service in the London Borough of Westminster. Factors likely to influence the use of Smart Parking services were identified from a literature review and incorporated into an explanatory model comprising 9 factors connected by 16 hypotheses. To test the model, residents of Westminster and visitors to the area were surveyed, resulting in a total of 212 valid responses. The responses were used to test a structural equation model, using confirmatory factor analysis. The results of the analysis indicated that Awareness of the scheme; Perceived Ease of Use; Perceived Usefulness; Cost saving; Perceived Privacy and Perceived Security all had a direct impact on Usage, with Awareness being the most influential factor. The results also highlighted the fact that, despite efforts by Westminster Council to publicise the scheme, 74% of respondents had little awareness of it, suggesting the need for improved publicity.

Attributed network embedding (ANE) attempts to represent a network in short code, while retaining information about node topological structures and node attributes. A node's feature and topological structure information could be divided into different local aspects, while in many cases, not all the information but part of the information contained in several local aspects determine the relations among different nodes. Most of the existing works barely concern and identify the aspect influence from network embedding to our knowledge. We attempt to use local embeddings to represent local aspect information and propose InfomaxANE which encodes both global and local embeddings from the perspective of mutual information. The local aspect embeddings are forced to learn and extract different aspect information from nodes' features and topological structures by using orthogonal constraint. A theoretical analysis is also provided to further confirm its correctness and rationality. Besides, to provide complete and refined information for local encoders, we also optimize feature aggregation in SAGE with different structures: feature similarities are concerned and aggregator is seperated from encoder. InfomaxANE is evaluated on both node clustering and node classification tasks (including both transductive and inductive settings) with several benchmark datasets, the results show the outperformance of InfomaxANE over competitive baselines. We also verify the significance of each module in our proposed InfomaxANE in the additional experiment.

Information from microblogs is gaining increasing attention from researchers interested in analyzing fluctuations in stock markets. Behavioral financial theory draws on social psychology to explain some of the irrational behaviors associated with financial decisions to help explain some of the fluctuations. In this study we argue that social media users who demonstrate an interest in finance can offer insights into ways in which irrational behaviors may affect a stock market. To test this, we analyzed all the data collected over a 3‐month period in 2011 from Tencent Weibo (one of the largest microblogging websites in China). We designed a social influence (SI)‐based Tencent finance‐related moods model to simulate investors' irrational behaviors, and designed a Tencent Moods‐based Stock Trend Analysis (TM_STA) model to detect correlations between Tencent moods and the Hushen‐300 index (one of the most important financial indexes in China). Experimental results show that the proposed method can help explain the data fluctuation. The findings support the existing behavioral financial theory, and can help to understand short‐term rises and falls in a stock market. We use behavioral financial theory to further explain our findings, and to propose a trading model to verify the proposed model.

Abstract This article reports on the development of a novel method for the analysis of Web logs. The method uses techniques that look for similarities between queries and identify sequences of “query transformation”. It allows sequences of query transformations to be represented as graphical networks, thereby giving a richer view of search behavior than is possible with the usual sequential descriptions. We also perform a basic analysis to study the correlations between observed transformation codes, with results that appear to show evidence of behavior habits. The method was developed using transaction logs from the Excite search engine to provide a tool for an ongoing research project that is endeavoring to develop a greater understanding of Web‐based searching by the general public.