Study on Enterprises’ Internet Public Opinion Area Hotspots Based on Social Network Analysis

With the rapid development of Web 2.0, online public opinion has become an issue in the companies’ development process. With numerous user-generated contents about real-world events generated almost in real-time, monitoring, evolution and management of online public opinion play the critical role for the healthy development of enterprises. By collecting articles about public opinion on the corporate network from CNKI and using Citespace based on social network analysis, we have combed the context of current research in this area, analyzed the characteristics of the current research on this topic, excavated research rules in this field and summarized research results to provide references for further study

Application of Big Data Technology in Product Quality and Safety Risk Information Monitoring

Product quality and safety risk monitoring with “risk management” as the core thought has been gradually introduced into the market supervision system. This paper discusses the application method of risk information collection and analysis in product quality risk monitoring through the application practice of big data technology in quality monitoring. Through the emotional analysis and judgment of the arti_x005fcle, the quality risk signal is intelligently excavated to provide technical means and monitoring methods for product quality risk monitoring

Determining citizens’ opinions about stories in the news media: analysing Google, Facebook and Twitter

We describe a method whereby a governmental policy maker can discover citizens’ reaction to news stories. This is particularly relevant in the political world, where governments’ policy statements are reported by the news media and discussed by citizens. The work here addresses two main questions: whereabouts are citizens discussing a news story, and what are they saying? Our strategy to answer the first question is to find news articles pertaining to the policy statements, then perform internet searches for references to the news articles’ headlines and URLs. We have created a software tool that schedules repeating Google searches for the news articles and collects the results in a database, enabling the user to aggregate and analyse them to produce ranked tables of sites that reference the news articles. Using data mining techniques we can analyse data so that resultant ranking reflects an overall aggregate score, taking into account multiple datasets, and this shows the most relevant places on the internet where the story is discussed. To answer the second question, we introduce the WeGov toolbox as a tool for analysing citizens’ comments and behaviour pertaining to news stories. We first use the tool for identifying social network discussions, using different strategies for Facebook and Twitter. We apply different analysis components to analyse the data to distil the essence of the social network users’ comments, to determine influential users and identify important comments

A linked data approach to sentiment and emotion analysis of twitter in the financial domain

Sentiment analysis has recently gained popularity in the financial domain thanks to its capability to predict the stock market based on the wisdom of the crowds. Nevertheless, current sentiment indicators are still silos that cannot be combined to get better insight about the mood of different communities. In this article we propose a Linked Data approach for modelling sentiment and emotions about financial entities. We aim at integrating sentiment information from different communities or providers, and complements existing initiatives such as FIBO. The ap- proach has been validated in the semantic annotation of tweets of several stocks in the Spanish stock market, including its sentiment information

Predicting Financial Crisis in Developing Economies: Astronomy or Astrology?

In the aftermath of the European currency crisis of 1992-3, the Mexican financial crisis of 1994-5 and the Asian financial crisis of 1997-8, neoclassical economists in the academy and policy community have been engaged in a project to develop predictors or indicators of currency, banking and generalized financial crises in developing economies. This paper critically examines the efforts of the economics profession in this regard on both empirical and theoretical grounds. The paper argues that these predictors perform poorly on empirical grounds--indeed, the predictors developed after each of these crises failed to predict the next major crisis. These predictors are also rejected on theoretical grounds. From a post-Keynesian perspective, there is no reason to expect that the mere provision of information will prevent crises by changing agents' behaviors. The paper will also propose several indicators that are consonant with post-Keynesian economic theory, although it will be argued that these indicators do not represent a sufficient means to prevent financial crisis. Ironically, as agents develop confidence in the predictive capacity of crisis indicators, they may engage in actions that increase the economy's vulnerability to crisis. Far more important to the project of preventing financial crisis in developing economies is the implementation of constraints on those investor behaviors that render liberalized, internationally integrated financial systems inherently prone to instability and crisis. Hence, intellectual capital would be more productively expended on devising appropriate changes in the overall regime in which investors operate (such as measures that compel changes in financing strategies) rather than in searching for new predictors of crisis.Financial Crisis