scholarly journals Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis

2018 ◽  
Vol 15 (9) ◽  
pp. 1877 ◽  
Author(s):  
Laia Subirats ◽  
Natalia Reguera ◽  
Antonio Bañón ◽  
Beni Gómez-Zúñiga ◽  
Julià Minguillón ◽  
...  
Keyword(s):  
Social Networks ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Rare Diseases ◽  
Temporal Analysis ◽  
Decision Makers ◽  
Political Decision ◽  
Data Word ◽  
Word Clouds ◽  
Log Likelihood

This research characterized how Facebook deals with rare diseases. This characterization included a content-based and temporal analysis, and its purpose was to help users interested in rare diseases to maximize the engagement of their posts and to help rare diseases organizations to align their priorities with the interests expressed in social networks. This research used Netvizz to download Facebook data, word clouds in R for text mining, a log-likelihood measure in R to compare texts and TextBlob Python library for sentiment analysis. The Facebook analysis shows that posts with photos and positive comments have the highest engagement. We also observed that words related to diseases, attention, disability and services have a lot of presence in the decalogue of priorities (which serves for all associations to work on the same objectives and provides the lines of action to be followed by political decision makers) and little on Facebook, and words of gratitude are more present on Facebook than in the decalogue. Finally, the temporal analysis shows that there is a high variation between the polarity average and the hour of the day.

scholarly journals Questions and features of automated social networks monitoring with support of intelligent user messages analysis.

2014 ◽  
Vol 2014 (4) ◽  
pp. 146-152 ◽  
Author(s):  
Александр Подвесовский ◽  
Aleksandr Podvesovskiy ◽  
Дмитрий Будыльский ◽  
Dmitriy Budylskiy
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Software Implementation ◽  
Network Data ◽  
Research Directions ◽  
Social Network Data ◽  
Monitoring Model

An opinion mining monitoring model for social networks introduced. The model includes text mining processing over social network data and uses sentiment analysis approach in particular. Practical usage results of software implementation and its requirements described as well as further research directions.

Unraveling E-WOM Patterns Using Text Mining and Sentiment Analysis

2020 ◽  
pp. 88-100
Author(s):  
João Guerreiro ◽  
Sandra Maria Correia Loureiro
Keyword(s):  
Decision Making ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Real Time ◽  
Word Of Mouth ◽  
Decision Makers ◽  
Valuable Resource ◽  
Textual Information ◽  
Online Reputation ◽  
Unstructured Information

Electronic word-of-mouth (e-WOM) is a very important way for firms to measure the pulse of its online reputation. Today, consumers use e-WOM as a way to interact with companies and share not only their satisfaction with the experience, but also their discontent. E-WOM is even a good way for companies to co-create better experiences that meet consumer needs. However, not many companies are using such unstructured information as a valuable resource to help in decision making: first, because e-WOM is mainly textual information that needs special data treatment and second, because it is spread in many different platforms and occurs in near-real-time, which makes it hard to handle. The current chapter revises the main methodologies used successfully to unravel hidden patterns in e-WOM in order to help decision makers to use such information to better align their companies with the consumer's needs.

scholarly journals Text Mining and Sentiment Analysis of Newspaper Headlines

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 414
Author(s):  
Arafat Hossain ◽  
Md. Karimuzzaman ◽  
Md. Moyazzem Hossain ◽  
Azizur Rahman
Keyword(s):  
Text Mining ◽  
Sentiment Analysis ◽  
Front Page ◽  
Text Analytics ◽  
Law And Order ◽  
Efficient Manner ◽  
Positive Type ◽  
Word Clouds ◽  
And Cluster Analysis ◽  
Political Turmoil

Text analytics are well-known in the modern era for extracting information and patterns from text. However, no study has attempted to illustrate the pattern and priorities of newspaper headlines in Bangladesh using a combination of text analytics techniques. The purpose of this paper is to examine the pattern of words that appeared on the front page of a well-known daily English newspaper in Bangladesh, The Daily Star, in 2018 and 2019. The elucidation of that era’s possible social and political context was also attempted using word patterns. The study employs three widely used and contemporary text mining techniques: word clouds, sentiment analysis, and cluster analysis. The word cloud reveals that election, kill, cricket, and Rohingya-related terms appeared more than 60 times in 2018, whereas BNP, poll, kill, AL, and Khaleda appeared more than 80 times in 2019. These indicated the country’s passion for cricket, political turmoil, and Rohingya-related issues. Furthermore, sentiment analysis reveals that words of fear and negative emotions appeared more than 600 times, whereas anger, anticipation, sadness, trust, and positive-type emotions came up more than 400 times in both years. Finally, the clustering method demonstrates that election, politics, deaths, digital security act, Rohingya, and cricket-related words exhibit similarity and belong to a similar group in 2019, whereas rape, deaths, road, and fire-related words clustered in 2018 alongside a similar-appearing group. In general, this analysis demonstrates how vividly the text mining approach depicts Bangladesh’s social, political, and law-and-order situation, particularly during election season and the country’s cricket craze, and also validates the significance of the text mining approach to understanding the overall view of a country during a particular time in an efficient manner.

Two Theories of Effective Government

2017 ◽  
Author(s):  
Johannes Lindvall
Keyword(s):  
Political Science ◽  
Political Institutions ◽  
Public Policies ◽  
Methodological Approach ◽  
Decision Makers ◽  
Precise Definition ◽  
Policy Changes ◽  
Political Decision ◽  
Definition Of ◽  
The Relationship

This chapter introduces the problem of “reform capacity” (the ability of political decision-makers to adopt and implement policy changes that benefit society as a whole, by adjusting public policies to changing economic, social, and political circumstances). The chapter also reviews the long-standing discussion in political science about the relationship between political institutions and effective government. Furthermore, the chapter explains why the possibility of compensation matters greatly for the politics of reform; provides a precise definition of the concept of reform capacity; describes the book's general approach to this problem; and discusses the ethics of compensating losers from reform; and presents the book's methodological approach.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

scholarly journals Are there similarities between the Corona and the climate crisis?

2021 ◽  
Author(s):  
Robert C. Schmidt
Keyword(s):  
Climate Change ◽  
Political Parties ◽  
Decision Procedures ◽  
Free Rider ◽  
Decision Makers ◽  
Short Paper ◽  
Political Decision ◽  
Look Back ◽  
Policy Measures ◽  
Climate Crisis

AbstractIn this short paper, I look back at the early stages of the Corona crisis, around early February 2020, and compare the situation with the climate crisis. Although these two problems unfold on a completely different timescale (weeks in the case of Corona, decades in the case of climate change), I find some rather striking similarities between these two problems, related with issues such as uncertainty, free-rider incentives, and disincentives of politicians to adequately address the respective issue with early, farsighted and possibly harsh policy measures. I then argue that for complex problems with certain characteristics, it may be necessary to establish novel political decision procedures that sidestep the normal, day-to-day political proceedings. These would be procedures that actively involve experts, and lower the involvement of political parties as far as possible to minimize the decision-makers’ disincentives.

Exploring Insights through Visualization of Association Rules from Text Mining Statistics

2013 ◽  
Vol 310 ◽  
pp. 567-571
Author(s):  
Arun Thotapalli Sundararaman
Keyword(s):  
Text Mining ◽  
Association Rules ◽  
Decision Makers ◽  
Analysis Of Knowledge ◽  
Strength Of Association ◽  
The Way

Visualization is an important technique for analysis of knowledge derived from text mining. While different approaches exist for visualization, this paper presents a novel way of visualizing the strength of association between multiple terms that summarizes association in the form of a matrix. This approach is expected to improve the way decision makers analyze insights from text mining.

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