Decision Making using Machine Learning based Opinion Prediction Model for Smart Governance

2019 ◽  
Vol 13 ◽  
Author(s):  
Akshi Kumar ◽  
Abhilasha Sharma
Keyword(s):  
Decision Making ◽  
Public Opinion ◽  
Policy Evaluation ◽  
Opinion Mining ◽  
Economic Status ◽  
Life Quality ◽  
Future Prospects ◽  
Indian Government ◽  
Data Set ◽  
Smart Governance

Background: Decision making requires a rigorous process of evaluation. Evaluation is an analytical and organized process to figure out the present positive influences, favourable future prospects, existing shortcomings and ulterior complexities of any plan, program, practice or a polity. Evaluation of policy is an essential and vital process required to measure the performance or progression of the scheme. The main purpose of policy evaluation is to empower various stakeholders and enhance their socio-economic environment. A large number of policies or schemes in different areas are launched/constituted by government in view of citizen welfare. Although, the governmental policies intends to better shape up the life quality of people but may also impact their everyday's life. Objective: So, the contemplation of public opinion plays a very significant role in the process of policy evaluation. The aim of this paper is to incorporate the concept of opinion mining in policy evaluation. An attempt has been made to elevate the process of policy evaluation by analyzing public opinion. Method: A latest governmental scheme Saubhagya launched by Indian government in 2017 has been selected for evaluation by applying supervised learning based opinion mining techniques. The data set of public opinion associated with this scheme has been captured by Twitter. Results: The result validates that the proposed methodology supports in optimizing the process of policy evaluation and provides a more accurate and actual status of policy's effect among Indian citizen. As a result, this would aid in identifying and implementing the preventive and corrective measures required to be taken for a successful policy. Conclusion : The proposed methodology will stabilize and strengthen the process of policy evaluation which target towards favourable and flourishing future prospects concerning the socio-economic status of a nation.

scholarly journals Sustainable governance in smart cities and use of supervised learning based opinion mining

2021 ◽  
Vol 11 (1) ◽  
pp. 498
Author(s):  
Hena Iqbal ◽  
Sujni Paul ◽  
Khaliquzzaman Khan
Keyword(s):  
Big Data ◽  
Policy Evaluation ◽  
Opinion Mining ◽  
Smart Cities ◽  
Life Quality ◽  
Future Prospects ◽  
Indian Government ◽  
Data Set ◽  
Governance Model

Evaluation is an analytical and organized process to figure out the present positive influences, favourable future prospects, existing shortcomings and ulterior complexities of any plan, program, practice or a policy. Evaluation of policy is an essential and vital process required to measure the performance or progression of the scheme. The main purpose of policy evaluation is to empower various stakeholders and enhance their socio-economic environment. A large number of policies or schemes in different areas are launched by government in view of citizen welfare. Although, the governmental policies intend to better shape up the life quality of people but may also impact their every day’s life. A latest governmental scheme Saubhagya launched by Indian government in 2017 has been selected for evaluation by applying opinion mining techniques. The data set of public opinion associated with this scheme has been captured by Twitter. The primary intent is to offer opinion mining as a smart city technology that harness the user-generated big data and analyse it to offer a sustainable governance model.

scholarly journals Sustainable governance in smart cities and use of supervised learning based opinion mining

2021 ◽  
Vol 11 (1) ◽  
pp. 489
Author(s):  
Hena Iqbal ◽  
Sujni Paul ◽  
Khaliquzzaman Khan
Keyword(s):  
Big Data ◽  
Policy Evaluation ◽  
Opinion Mining ◽  
Smart Cities ◽  
Life Quality ◽  
Future Prospects ◽  
Indian Government ◽  
Data Set ◽  
Governance Model

Evaluation is an analytical and organized process to figure out the present positive influences, favourable future prospects, existing shortcomings and ulterior complexities of any plan, program, practice or a policy. Evaluation of policy is an essential and vital process required to measure the performance or progression of the scheme. The main purpose of policy evaluation is to empower various stakeholders and enhance their socio-economic environment. A large number of policies or schemes in different areas are launched by government in view of citizen welfare. Although, the governmental policies intend to better shape up the life quality of people but may also impact their every day’s life. A latest governmental scheme Saubhagya launched by Indian government in 2017 has been selected for evaluation by applying opinion mining techniques. The data set of public opinion associated with this scheme has been captured by Twitter. The primary intent is to offer opinion mining as a smart city technology that harness the user-generated big data and analyse it to offer a sustainable governance model.

scholarly journals Random Forest: A Hybrid Implementation for Sarcasm Detection in Public Opinion Mining

2019 ◽  
Vol 8 (12) ◽  
pp. 5022-5025
Keyword(s):  
Decision Making ◽  
Public Opinion ◽  
Random Forest ◽  
Decision Tree ◽  
Opinion Mining ◽  
Random Forest Classifier ◽  
Decision Tree Classifier ◽  
Wrong Decision ◽  
Tree Classifier ◽  
Better Than

Modelling the sentiment with context is one of the most important part in Sentiment analysis. There are various classifiers which helps in detecting and classifying it. Detection of sentiment with consideration of sarcasm would make it more accurate. But detection of sarcasm in people review is a challenging task and it may lead to wrong decision making or classification if not detected. This paper uses Decision Tree and Random forest classifiers and compares the performance of both. Here we consider the random forest as hybrid decision tree classifier. We propose that performance of random forest classifier is better than any other normal decision tree classifier with appropriate reasoning

scholarly journals Analysis of College Students’ Public Opinion Based on Machine Learning and Evolutionary Algorithm

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Jinqing Zhang ◽  
Pengchao Zhang ◽  
Bin Xu
Keyword(s):  
College Students ◽  
Public Opinion ◽  
Opinion Mining ◽  
Design Procedure ◽  
Data Set ◽  
Comprehensive Information ◽  
Feature Importance ◽  
State Of Mind ◽  
Negative Impacts ◽  
Value Decomposition

The recent information explosion may have many negative impacts on college students, such as distraction from learning and addiction to meaningless and fake news. To avoid these phenomena, it is necessary to verify the students’ state of mind and give them appropriate guidance. However, many peculiarities, including subject focused, multiaspect, and low consistency on different samples’ interests, bring great challenges while leveraging the mainstream opinion mining method. To solve this problem, this paper proposes a new way by using a questionnaire which covers most aspects of a student’s life to collect comprehensive information and feed the information into a neural network. With reliable prediction on students’ state of mind and awareness of feature importance, colleges can give students guidance associated with their own experience and make macroscopic policies more effective. A pipeline is proposed to relieve overfitting during the collected information training. First, the singular value decomposition is used in pretreatment of data set which includes outlier detection and dimension reduction. Then, the genetic algorithm is introduced in the training process to find the proper initial parameters of network, and in this way, it can prevent the network from falling into the local minimum. A method of calculating the importance of students’ features is also proposed. The experiment result shows that the new pipeline works well, and the predictor has high accuracy on predicting fresh samples. The design procedure and the prediction design will provide suggestions to deal with students’ state of mind and the college’s public opinion.

scholarly journals Arousability, Personality, and Decision-Making Ability in Dissociative Disorder

2021 ◽  
pp. 025371762098155
Author(s):  
Doyel Ghosh ◽  
Pritha Mukhopadhyay ◽  
Ishani Chatterjee ◽  
Prasanta Kumar Roy
Keyword(s):  
Decision Making ◽  
Iowa Gambling Task ◽  
Psychiatric Patients ◽  
Economic Status ◽  
Reaction Time Task ◽  
Conversion Disorder ◽  
Control Group ◽  
Cross Sectional ◽  
Personality Disposition

Background: There is a gap in understanding the pathogenesis of dissociative conversion disorder (DCD), despite the disorder having a strong historical root. The role of personality and neurocognitive factors are now highlighted; however, inconsistencies are reported. This study explores the personality disposition, arousability, and decision-making ability of patients with DCD, in reference to a healthy control group (HCG). Methods: In this cross-sectional study, the sample comprised ten adult psychiatric patients with DCD. Ten participants of the HCG were matched according to age, gender, education, economic status, domicile, religious background, and handedness. The study assessed personality disposition with Temperament and Character Inventory, arousability with reaction time task, and decision-making ability with the Iowa Gambling Task (IGT PEBL version). Results: The DCD group differed significantly on personality disposition related to both temperament and character. There was also evidence of easy arousability and frustration along with deficit in executive function related to decision-making ability. Conclusion: This study highlights the presence of both temperamental and characterological factors associated with DCD. Moreover, this study identifies the role of cognitive arousability and decision-making or feedback utilization ability in the psychopathology of DCD.

Grundrechtliche Einordnung sozialer Netzwerke vor dem Hintergrund des Netzwerkdurchsetzungsgesetzes (NetzDG)

2021 ◽  
Author(s):  
Katrin Giere
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Public Opinion ◽  
Scientific Research ◽  
Political Decision ◽  
Media Freedom ◽  
Penal Law ◽  
Political Decision Making

The influence on public opinion of social networks such as Facebook and Twitter regarding the process of political decision-making is constantly evolving. However, the discussion whether these networks are holders of the fundamental right of media freedom is still in its "infancy stage". This piece takes up this topic, which is practically relevant, but still lacks adequate scientific research. Against this background, the paper addresses the Network Enforcement Act (NetzDG) which came into effect in Germany on 1 October 2017. With this law, the federal legislature has imposed proactive inspection obligations on certain providers of social networks. Operators are now legally required to check contents to ensure it does not violate German penal law.

Mimoprávní vlivy na rozhodování českého Ústavního soudu

2021 ◽  
Author(s):  
Hubert Smekal ◽  
Jaroslav Benák ◽  
Monika Hanych ◽  
Ladislav Vyhnánek ◽  
Štěpán Janků
Keyword(s):  
Decision Making ◽  
Public Opinion ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Constitutional Court ◽  
Judicial Decision ◽  
Judicial Decision Making ◽  
The Media ◽  
Book Studies ◽  
The Impact

The book studies other than purely legal factors that influence the Czech Constitutional Court judges in their decision-making. The publication is inspired by foreign models of judicial decision-making and discusses their applicability in the Czech environment. More specifically, it focuses, for example, on the influence of the judge’s personality, collegiality, strategic decision-making or the impact of public opinion and the media. The book is based mainly on interviews with current constitutional judges.

Inside Trending Topic Algorithm: How Do Human Interactions Drive Public Opinion in an Artificial Environment

2021 ◽  
pp. 089443932110415
Author(s):  
Vanessa Russo ◽  
Emiliano del Gobbo
Keyword(s):  
Public Opinion ◽  
Semantic Network ◽  
Opinion Leader ◽  
Computational Techniques ◽  
Data Sets ◽  
Process Data ◽  
Data Set ◽  
Social Actors ◽  
Human Interactions ◽  
Artificial Environment

The object of this research is to exploit the algorithm of Twitter’s trending topic (TT) and identify the elements capable of guiding public opinion in the Italian panorama. The underlying hypotheses that guide the whole article, confirmed by the research results, concern the existence of (a) a limited number of elements at the base of each popular hashtag with very high viral power and (b) hashtags transversal to the themes detected by the Twitter algorithm that define specific opinion polls. Through computational techniques, it was possible to extract and process data sets from six specific hashtags highlighted by TT. In a first step through social network analysis, we analyzed the hashtag semantic network to identify the hashtags transversal to the six TTs. Subsequently, we selected for each data set the contents with high sharing power and created a “potential opinion leader” index to identify users with influencer characteristics. Finally, a cross section of social actors able to guide public opinion in the Twittersphere emerged from the intersection between potentially influential users and the viral contents.

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