Disaggregating “China, Inc.”: The Hierarchical Politics of WTO Entry

2020 ◽  
Vol 53 (13) ◽  
pp. 2118-2152
Author(s):  
Yeling Tan
Keyword(s):  
Text Analysis ◽  
Industrial Base ◽  
Policy Responses ◽  
Machine Learning Methods ◽  
Original Dataset ◽  
Economic Strategies ◽  
Textual Data ◽  
Chinese Industry ◽  
Market Shaping ◽  
Subnational Governments

How does state structure affect responses to globalization? This article examines why some parts of the Chinese state enacted more liberalizing policies than others in response to World Trade Organization (WTO) entry. It shows that, despite single-party rule, China’s WTO-era policy trajectories were neither top-down nor monolithic. Instead, central and subnational governments diverged in their policy responses. The study identifies three competing economic strategies from which these responses are drawn: market-replacing (directive), market-shaping (developmental), and market-enhancing (regulatory). The analysis uses an original dataset of Chinese industry regulations from 1978 to 2014 and employs machine learning methods in text analysis to identify words associated with each strategy. Combining tariff, industry, and textual data, the article demonstrates that the divergent strategies adopted by central and subnational governments are driven by each unit’s differential accountability to the WTO and by the diversity of that unit’s industrial base.

Studying Political Decision Making With Automatic Text Analysis

2019 ◽  
Author(s):  
Wouter van Atteveldt ◽  
Kasper Welbers ◽  
Mariken van der Velden
Keyword(s):  
Political Science ◽  
Text Analysis ◽  
Political Ideology ◽  
Numerical Representation ◽  
Political Decision ◽  
Text Collections ◽  
Textual Data ◽  
Automatic Text Analysis ◽  
Political Decision Making ◽  
Automatic Text

Analyzing political text can answer many pressing questions in political science, from understanding political ideology to mapping the effects of censorship in authoritarian states. This makes the study of political text and speech an important part of the political science methodological toolbox. The confluence of increasing availability of large digital text collections, plentiful computational power, and methodological innovations has led to many researchers adopting techniques of automatic text analysis for coding and analyzing textual data. In what is sometimes termed the “text as data” approach, texts are converted to a numerical representation, and various techniques such as dictionary analysis, automatic scaling, topic modeling, and machine learning are used to find patterns in and test hypotheses on these data. These methods all make certain assumptions and need to be validated to assess their fitness for any particular task and domain.

Estimating numerical scale ratings from text-based service reviews

2020 ◽  
Vol 31 (2) ◽  
pp. 187-202
Author(s):  
Hsiu-Yuan Tsao ◽  
Ming-Yi Chen ◽  
Colin Campbell ◽  
Sean Sands
Keyword(s):  
Language Processing ◽  
Text Analysis ◽  
Design Methodology ◽  
Survey Methods ◽  
Content Type ◽  
Consumer Review ◽  
Numerical Scale ◽  
Analysis Technique ◽  
Textual Data ◽  
Practical Implications

PurposeThis paper develops a generalizable, machine-learning-based method for measuring established marketing constructs using passive analysis of consumer-generated textual data from service reviews. The method is demonstrated using topic and sentiment analysis along dimensions of an existing scale: lodging quality index (LQI).Design/methodology/approachThe method induces numerical scale ratings from text-based data such as consumer reviews. This is accomplished by automatically developing a dictionary from words within a set of existing scale items, rather a more manual process. This dictionary is used to analyze textual consumer review data, inducing topic and sentiment along various dimensions. Data produced is equivalent with Likert scores.FindingsPaired t-tests reveal that the text analysis technique the authors develop produces data that is equivalent to Likert data from the same individual. Results from the authors’ second study apply the method to real-world consumer hotel reviews.Practical implicationsResults demonstrate a novel means of using natural language processing in a way to complement or replace traditional survey methods. The approach the authors outline unlocks the ability to rapidly and efficiently analyze text in terms of any existing scale without the need to first manually develop a dictionary.Originality/valueThe technique makes a methodological contribution by outlining a new means of generating scale-equivalent data from text alone. The method has the potential to both unlock entirely new sources of data and potentially change how service satisfaction is assessed and opens the door for analysis of text in terms of a wider range of constructs.

Summarizing Opinions with Sentiment Analysis from Multiple Reviews on Travel Destinations

2018 ◽  
Vol 9 (2) ◽  
pp. 111-120
Author(s):  
Argha Roy ◽  
Shyamali Guria ◽  
Suman Halder ◽  
Sayani Banerjee ◽  
Sourav Mandal
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Efficient Method ◽  
Text Analysis ◽  
Human Life ◽  
Analysis Techniques ◽  
Classification Technique ◽  
Textual Data ◽  
The Web

Recently, the web has been crowded with growing volumes of various texts on every aspect of human life. It is difficult to rapidly access, analyze, and compose important decisions using efficient methods for raw textual data in the form of social media, blogs, feedback, reviews, etc., which receive textual inputs directly. It proposes an efficient method for summarization of various reviews of tourists on a specific tourist spot towards analyzing their sentiments towards the place. A classification technique automatically arranges documents into predefined categories and a summarization algorithm produces the exact condensed input such that output is most significant concepts of source documents. Finally, sentiment analysis is done in summarized opinion using NLP and text analysis techniques to show overall sentiment about the spot. Therefore, interested tourists can plan to visit the place do not go through all the reviews, rather they go through summarized documents with the overall sentiment about target place.

scholarly journals Use of artificial intelligence technologies for building individual educational trajectories of students

2021 ◽  
Vol 18 (1) ◽  
pp. 27-35
Author(s):  
Roman B. Kupriyanov ◽  
Dmitry L. Agranat ◽  
Ruslan S. Suleymanov
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Recommendation System ◽  
Educational Process ◽  
Content Filtering ◽  
Machine Learning Methods ◽  
Educational Trajectories ◽  
Textual Data ◽  
Second Year

Problem and goal. Developed and tested solutions for building individual educational trajectories of students, focused on improving the educational process by forming a personalized set of recommendations from the optional disciplines. Methodology. Data mining and machine learning methods were used to process both numeric and textual data. The approaches based on collaborative and content filtering to generate recommendations for students were also used. Results. Testing of the developed system was carried out in the context of several periods of elective courses selection, in which 4,769 first- and second-year students took part. A set of recommendations was automatically generated for each student, and then the quality of the recommendations was evaluated based on the percentage of students who used these recommendations. According to the results of testing, the recommendations were used by 1,976 students, which was 41.43% of the total number of participants. Conclusion. In the study, a recommendation system was developed that performs automatic ranking of subjects of choice and forms a personalized set of recommendations for each student based on their interests for building individual educational trajectories.

scholarly journals National interests and coalition positions on climate change: A text-based analysis

2020 ◽  
pp. 019251212095353
Author(s):  
Paula Castro
Keyword(s):  
Climate Change ◽  
Developing Countries ◽  
Text Analysis ◽  
Bargaining Power ◽  
International Negotiations ◽  
National Interests ◽  
Policy Responses ◽  
Level I ◽  
Automatic Text Analysis ◽  
Automatic Text

Coalitions play a central role in the international negotiations under the United Nations Framework Convention on Climate Change. By getting together, countries join resources in defending their interests and positions. But building coalitions may come at a cost. Coalition positions are a result of compromise between their members, and thus the increase in bargaining power may come at a price if the preferences of their members are heterogeneous. Relying on automatic text analysis of written position papers submitted to the negotiations, I analyze the extent to which coalitions represent the preferences of their members and discuss whether this contributes to disproportionate policy responses at the international level. I focus on a recently formed coalition: the Like-Minded Developing Countries, a large and heterogeneous group that brings together emerging, oil-dependent and poor developing countries.

Uniting the Tribes: Using Text for Marketing Insight

2019 ◽  
Vol 84 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Jonah Berger ◽  
Ashlee Humphreys ◽  
Stephan Ludwig ◽  
Wendy W. Moe ◽  
Oded Netzer ◽  
...  
Keyword(s):  
Customer Service ◽  
Text Analysis ◽  
External Validity ◽  
Textual Analysis ◽  
Online Reviews ◽  
Marketing Communications ◽  
Press Releases ◽  
Textual Data ◽  
Future Work ◽  
Internal And External Validity

Words are part of almost every marketplace interaction. Online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data. But how can marketers best use such data? This article provides an overview of automated textual analysis and details how it can be used to generate marketing insights. The authors discuss how text reflects qualities of the text producer (and the context in which the text was produced) and impacts the audience or text recipient. Next, they discuss how text can be a powerful tool both for prediction and for understanding (i.e., insights). Then, the authors overview methodologies and metrics used in text analysis, providing a set of guidelines and procedures. Finally, they further highlight some common metrics and challenges and discuss how researchers can address issues of internal and external validity. They conclude with a discussion of potential areas for future work. Along the way, the authors note how textual analysis can unite the tribes of marketing. While most marketing problems are interdisciplinary, the field is often fragmented. By involving skills and ideas from each of the subareas of marketing, text analysis has the potential to help unite the field with a common set of tools and approaches.

scholarly journals Pandemic Politics: Timing State-Level Social Distancing Responses to COVID-19

2020 ◽  
Author(s):  
Christopher Adolph ◽  
Kenya Amano ◽  
Bree Bang-Jensen ◽  
Nancy Fullman ◽  
John Wilkerson
Keyword(s):  
Event History Analysis ◽  
State Level ◽  
The United States ◽  
The Novel ◽  
Social Distancing ◽  
Policy Responses ◽  
Original Dataset ◽  
Political Partisanship ◽  
History Analysis ◽  
Novel Coronavirus

AbstractSocial distancing policies are critical but economically painful measures to flatten the curve against emergent infectious diseases. As the novel coronavirus that causes COVID-19 spread throughout the United States in early 2020, the federal government issued social distancing recommendations but left to the states the most difficult and consequential decisions restricting behavior, such as canceling events, closing schools and businesses, and issuing stay-at-home orders. We present an original dataset of state-level social distancing policy responses to the epidemic and explore how political partisanship, COVID-19 caseload, and policy diffusion explain the timing of governors’ decisions to mandate social distancing. An event history analysis of five social distancing policies across all fifty states reveals the most important predictors are political: all else equal, Republican governors and governors from states with more Trump supporters were slower to adopt social distancing policies. These delays are likely to produce significant, on-going harm to public health.

scholarly journals Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text

2021 ◽  
Vol 11 (17) ◽  
pp. 8172
Author(s):  
Jebran Khan ◽  
Sungchang Lee
Keyword(s):  
Social Media ◽  
Text Analysis ◽  
State Of The Art ◽  
Context Aware ◽  
Intended Meaning ◽  
Text Data ◽  
Recall Accuracy ◽  
Wide Range ◽  
Textual Data ◽  
Noisy Text

We proposed an application and data variations-independent, generic social media Textual Variations Handler (TVH) to deal with a wide range of noise in textual data generated in various social media (SM) applications for enhanced text analysis. The aim is to build an effective hybrid normalization technique that ensures the use of useful information of the noisy text in its intended form instead of filtering them out to analyze SM text better. The proposed TVH performs context-aware text normalization based on intended meaning to avoid the wrong word substitution. We integrate the TVH with state-of-the-art (SOTA) deep-learning-based text analysis methods to enhance their performance for noisy SM text data. The proposed scheme shows promising improvement in the text analysis of informal SM text in terms of precision, recall, accuracy, and F1-score in simulation.

Summarizing Opinions with Sentiment Analysis from Multiple Reviews on Travel Destinations

2020 ◽  
pp. 310-320
Author(s):  
Argha Roy ◽  
Shyamali Guria ◽  
Suman Halder ◽  
Sayani Banerjee ◽  
Sourav Mandal
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Efficient Method ◽  
Text Analysis ◽  
Human Life ◽  
Analysis Techniques ◽  
Classification Technique ◽  
Textual Data ◽  
The Web

Recently, the web has been crowded with growing volumes of various texts on every aspect of human life. It is difficult to rapidly access, analyze, and compose important decisions using efficient methods for raw textual data in the form of social media, blogs, feedback, reviews, etc., which receive textual inputs directly. It proposes an efficient method for summarization of various reviews of tourists on a specific tourist spot towards analyzing their sentiments towards the place. A classification technique automatically arranges documents into predefined categories and a summarization algorithm produces the exact condensed input such that output is most significant concepts of source documents. Finally, sentiment analysis is done in summarized opinion using NLP and text analysis techniques to show overall sentiment about the spot. Therefore, interested tourists can plan to visit the place do not go through all the reviews, rather they go through summarized documents with the overall sentiment about target place.

Sign in / Sign up

Export Citation Format

Share Document