Predicting Dependency of Approval Rating Change from Twitter Activity and Sentiment Analysis

2020 ◽  
pp. 103-112
Author(s):  
Demijan Grgić ◽  
Mislav Karaula ◽  
Marina Bagić Babac ◽  
Vedran Podobnik
Keyword(s):  
Sentiment Analysis ◽  
Approval Rating ◽  
Twitter Activity ◽  
Rating Change

scholarly journals Deep Learning-Based Election Results Prediction Using Twitter Activity

2021 ◽  
Author(s):  
Haider Ali ◽  
Haleem Farman ◽  
Hikmat Yar ◽  
Zahid Khan ◽  
Shabana Habib ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
General Election ◽  
Learning Approaches ◽  
Social Media Platforms ◽  
General Elections ◽  
Election Results ◽  
Twitter Activity ◽  
The Stability

Abstract Nowadays, political parties have widely adopted social media for their party promotions and election campaigns. During the election, Twitter and other social media platforms are used for political coverage to promote the party and its candidates. This research discusses and estimates the stability of many volumetric social media approaches to forecast election results from social media activities. Numerous machine learning approaches are applied to opinions shared on social media for predicting election results. This paper presents a machine learning model based on sentiment analysis to predict Pakistan's general election results. In a general election, voters vote for their favorite party or candidate based on their personal interests. Social media has been extensively used for the campaign in Pakistan general election 2018. Using a machine learning technique, we provide a five-step process to analyze the overall election results, whether fair or unfair. The work is concluded with detailed experimental results and a discussion on the outcomes of sentiment analysis for real-world forecasting and approval for general elections in Pakistan.

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

An historical analysis of species references in American English

Corpora ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. 327-349
Author(s):  
Craig Frayne
Keyword(s):  
Environmental Change ◽  
Sentiment Analysis ◽  
Quantitative Methods ◽  
English Language ◽  
Language Use ◽  
American English ◽  
Historical Analysis ◽  
The Past ◽  
Corpus Studies ◽  
Google Books

This study uses the two largest available American English language corpora, Google Books and the Corpus of Historical American English (coha), to investigate relations between ecology and language. The paper introduces ecolinguistics as a promising theme for corpus research. While some previous ecolinguistic research has used corpus approaches, there is a case to be made for quantitative methods that draw on larger datasets. Building on other corpus studies that have made connections between language use and environmental change, this paper investigates whether linguistic references to other species have changed in the past two centuries and, if so, how. The methodology consists of two main parts: an examination of the frequency of common names of species followed by aspect-level sentiment analysis of concordance lines. Results point to both opportunities and challenges associated with applying corpus methods to ecolinguistc research.

Investigating the President

2016 ◽  
Author(s):  
Douglas L. Kriner ◽  
Eric Schickler
Keyword(s):  
Public Policy ◽  
Political Theater ◽  
Policy Outcomes ◽  
Presidential Power ◽  
Political Pressure ◽  
Comprehensive Overview ◽  
Partisan Polarization ◽  
Approval Rating ◽  
Congressional Investigations ◽  
Over Time

Although congressional investigations have provided some of the most dramatic moments in American political history, they have often been dismissed as mere political theater. But these investigations are far more than grandstanding. This book shows that congressional investigations are a powerful tool for members of Congress to counter presidential aggrandizement. By shining a light on alleged executive wrongdoing, investigations can exert significant pressure on the president and materially affect policy outcomes. This book constructs the most comprehensive overview of congressional investigative oversight to date, analyzing nearly 13,000 days of hearings, spanning more than a century, from 1898 through 2014. The book examines the forces driving investigative power over time and across chambers, and identifies how hearings might influence the president’s strategic calculations through the erosion of the president’s public approval rating, and uncover the pathways through which investigations have shaped public policy. Put simply, by bringing significant political pressure to bear on the president, investigations often afford Congress a blunt, but effective check on presidential power—without the need to worry about veto threats or other hurdles such as Senate filibusters. In an era of intense partisan polarization and institutional dysfunction, the book delves into the dynamics of congressional investigations and how Congress leverages this tool to counterbalance presidential power.

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