Intelligent Learning based Opinion Mining Model for Governmental Decision Making

Sharma, Abhilasha; Shekhar, Himanshu

doi:10.1016/j.procs.2020.06.026

Cited by 20 publications

(5 citation statements)

References 3 publications

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“…-Determine 𝐾 (cluster center) as the number of clusters. -Calculate the distance between vector data (x) and the cluster center in (1).…”

Section: K-means Clusteringmentioning

confidence: 99%

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Success factors for citizen-based government decision making using K-means fuzzy learning vector quantization

Utomo,

Adi,

Nurhayati

2023

IJEECS

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<span>The Indonesian government often needs assistance in making citizen-based decisions, for example selecting work program plans. Residents have their criteria in the forum to choose a work program plan. This study proposes the K-means fuzzy learning vector quantization (FLVQ) methods to select citizen-based government decision-making criteria. The K-means FLVQ method has never been used to assist government decision-making. However, citizen criteria can be a success factor for government decision-making. The selection of criteria begins with data collection from forum participants. The results of data collection get 11 criteria. Then, the K-means FLVQ method carries out labeling and classification. The addition of the K-means process in the selection criteria can provide optimal results. Citizens can give assessment criteria freely. Then the assessment of citizens is classified by FLVQ. The classification results obtained seven criteria, namely: i) urgency, ii) sustainability, iii) priority, vi) usability, v) prosperity, vi) comfortability, and vii) artistic. Governments can use these criteria to make decisions about planned work programs. The criteria selection algorithm was also evaluated using the confusion matrix method with an accuracy of 88% and an error of 12%.</span>

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“…-Determine 𝐾 (cluster center) as the number of clusters. -Calculate the distance between vector data (x) and the cluster center in (1).…”

Section: K-means Clusteringmentioning

confidence: 99%

“…Government is a group of organizations. The government must listen to citizens' opinions [1]. Citizen participation helps the government to make decisions [2]- [4].…”

Section: Introductionmentioning

confidence: 99%

Success factors for citizen-based government decision making using K-means fuzzy learning vector quantization

Utomo,

Adi,

Nurhayati

2023

IJEECS

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“…Growth towards e-governance and social networks has been an important driver for public participation, transparent and collaborative governance [12]. The adoption of social media in governance has been widely studied for its effectiveness in the dissemination and communication of information as opposed to traditional government websites [29], to provide information on opinions of citizens towards government decisions and policies [24], its influence on citizens' perception and behaviour [11] and its effectiveness during crisis management [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Semi-supervised learning approaches for predicting South African political sentiment for local government elections

Mashadi¹,

Marivate²

2022

Preprint

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This study aims to understand the South African political context by analysing the sentiments shared on Twitter during the local government elections. An emphasis on the analysis was placed on understanding the discussions led around four predominant political parties -ANC, DA, EFF and ActionSA. A semi-supervised approach by means of a graph-based technique to label the vast accessible Twitter data for the classification of tweets into negative and positive sentiment was used. The tweets expressing negative sentiment were further analysed through latent topic extraction to uncover hidden topics of concern associated with each of the political parties.Our findings demonstrated that the general sentiment across South African Twitter users is negative towards all four predominant parties with the worst negative sentiment among users projected towards the current ruling party, ANC, relating to concerns cantered around corruption, incompetence and loadshedding.

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“…Intelligent governance as a new governance paradigm can help the Canadian government adapt to the new environment and overcome extraordinary dysfunctions. Literature [6] mined the opinion of users who tweet on Twitter about the government, and based on the results of the analysis went to better help government agencies to make feedback. Using the model to analyze demographic opinions can suggest solutions for intelligent governance and government interaction policies.…”

Section: Introductionmentioning

confidence: 99%

Civil and Commercial Jurisprudence Observations on Intelligent Social Governance

Geng

2023

Applied Mathematics and Nonlinear Sciences

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The purpose of civil and commercial jurisprudence observation is to realize the intelligent and innovative development of social governance and to improve the efficiency of related cases. This paper develops a civil and commercial jurisprudence case information analysis system that utilizes MVC architecture and intelligent technology. The BERT pre-training model and self-training model are combined for the construction of a civil and commercial jurisprudence legal information extraction model, which helps judicial personnel understand the scope of civil and commercial jurisprudence cases in the process of social governance. Then, the ILJP is used as the basis to combine the fact encoder, the element predictor and the legal cause predictor to carry out the construction of the interpretable legal cause intelligent research and judgment model, which provides an intelligent research and judgment program for the civil and commercial jurisprudence social governance cases. The effectiveness of the civil and commercial law case analysis system is an important guarantee to enhance intelligent social governance, so this paper carries out a test and analysis of its intelligent algorithm and system performance. The results show that the legal information extraction model tends to stabilize after the first 15 iterations of training, and its validation loss value is around 0.173, and the training loss value of the model is around 0.008 when the training of the intelligent research and judgment model reaches 200 rounds. When the information analysis system of civil and commercial law cases reaches 1,000 concurrent users, the average response time of each logical operation does not exceed 3s, and the occupancy rate of CPU and memory resources is over 30%. The information age needs to fully combine technology and social governance in order to deeply mine the information of civil and commercial jurisprudence related cases, improve the efficiency of social governance, and innovate the intelligent development of social governance.

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Intelligent Learning based Opinion Mining Model for Governmental Decision Making

Cited by 20 publications

References 3 publications

Success factors for citizen-based government decision making using K-means fuzzy learning vector quantization

Success factors for citizen-based government decision making using K-means fuzzy learning vector quantization

Semi-supervised learning approaches for predicting South African political sentiment for local government elections

Civil and Commercial Jurisprudence Observations on Intelligent Social Governance

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