A quantitative text analysis of artificial intelligence industry policy in China

2020 ◽  
Vol 57 (1) ◽  
Author(s):  
Lin Wang ◽  
Lixuan Zhang
Keyword(s):  
Artificial Intelligence ◽  
Text Analysis ◽  
Quantitative Text Analysis ◽  
Industry Policy

scholarly journals Opinion Mining and Sentiment Analysis on Big Data

2020 ◽  
pp. 410-415
Author(s):  
Shruti Rajkumar Choudhary
Keyword(s):  
Artificial Intelligence ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Opinion Mining ◽  
Stock Exchange ◽  
Daily Basis ◽  
Text Data ◽  
The Public ◽  
Public Sentiment

<p>Opinion mining is extract subjective information from text data using tools such as NLP, text analysis etc. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product.In this project the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in terms of positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.</p>

scholarly journals Towards Holistic Governance of China’s E-Waste Recycling: Evolution of Networked Policies

2020 ◽  
Vol 17 (20) ◽  
pp. 7407
Author(s):  
Xiuli Yang ◽  
Xin Miao ◽  
Jinli Wu ◽  
Ziwei Duan ◽  
Rui Yang ◽  
...  
Keyword(s):  
Content Analysis ◽  
Text Analysis ◽  
Electronic Waste ◽  
Waste Recycling ◽  
Chinese Government ◽  
Central Question ◽  
Electronic Products ◽  
Policy Documents ◽  
Quantitative Text Analysis ◽  
Evolution Tendency

Electronic products are being updated and replaced much faster and there is therefore an increasing growth in electronic waste (e-waste). In order to promote professional recycling of e-waste, the relevant government departments of China have published a series of policies. This paper aims to unearth the evolution tendency of the networked policies towards holistic governance of China’s e-waste recycling. Content analysis, quantitative text analysis and network analysis are applied to analyze relevant policy documents from 2001 to 2016. This paper illustrates evolution of policy themes, evolution of intergovernmental relationships, and evolution of policy relations. This study reveals policy intentions, maps policy progress, and unearths governance philosophy, providing an overall understanding of the policy ways by which the Chinese government has deployed its guiding strategies on professional recycling of e-waste. This paper illustrates how to approach holistic governance from perspective of networked policies, contributing to answering the central question of holistic governance about how to achieve it.

scholarly journals Framing the mass media: Exploring fake news as a frame embedded in political discourse

2019 ◽  
Vol 4 (1) ◽  
pp. 57-76
Author(s):  
Jan R. Riebling ◽  
Ina von der Wense
Keyword(s):  
Social Context ◽  
Mass Media ◽  
Text Analysis ◽  
Political Discourse ◽  
Alternative Media ◽  
Political Orientation ◽  
Fake News ◽  
Recent Growth ◽  
News Frame ◽  
Quantitative Text Analysis

The recent growth of alternative media sites and sources has also seen the rise of an aggressive rhetoric decrying mass media or parts thereof as being untrustworthy and politically biased. While it is unclear whether the fake news debate is directly connected with this, it is surely a framing of mass media. In this article, we use techniques of quantitative text analysis in order to analyse how the fake news frame is structured and to understand its central determinants in terms of social context and political orientation. Using quantitative text analysis, we analyse the frame usage and semantic embeddedness in eight blogs. We find evidence for a generalised frame that tends to be independent of political orientation of the blog.

scholarly journals Coalition Governments and Party Competition: Political Communication Strategies of Coalition Parties

2015 ◽  
Vol 5 (2) ◽  
pp. 333-349 ◽  
Author(s):  
Iñaki Sagarzazu ◽  
Heike Klüver
Keyword(s):  
Political Communication ◽  
Text Analysis ◽  
Communication Strategies ◽  
Party Competition ◽  
Policy Agenda ◽  
Press Releases ◽  
Coalition Governments ◽  
Electoral Cycle ◽  
Close Proximity ◽  
Quantitative Text Analysis

Coalition parties have to reconcile two competing logics: they need to demonstrate unity to govern together, but also have to emphasize their own profile to succeed in elections. We argue that the electoral cycle explains whether unity or differentiation prevails. While differentiation dominates at the beginning and the end of the legislative term in close proximity to elections, compromise dominates the middle of the term when coalition governments focus on enacting a common policy agenda. To test our theoretical claims, we draw on an innovative quantitative text analysis of more than 21,000 press releases published by coalition parties from 2000 until 2010.

Assignments with the Computer

1986 ◽  
Vol 16 (3) ◽  
pp. 235-247
Author(s):  
William Dennis Horn
Keyword(s):  
Artificial Intelligence ◽  
Text Analysis ◽  
Word Processing ◽  
Technical Writing ◽  
Writing Classroom ◽  
Job Market ◽  
Computerized Instruction ◽  
Technical Writers ◽  
The Way

The current job market favors young technical writers who are skilled in the way of the computer both as a subject of writing and as a production tool. In the technical writing classroom students can be exposed to this important technology through assignments that include computerized instruction, word processing, text analysis, artificial intelligence, and communications.

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