A Proposed Framework for Evaluating the Performance of Government Initiatives Through Sentiment Analysis

Dandannavar, Padma; Mangalwede, S. R.; Deshpande, S. B.

doi:10.1007/978-981-13-0617-4_32

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…Since most Indonesian society uses Bahasa Indonesia, this research will be scraping the data in Bahasa Indonesia. The proposed framework used in this research consists of data collection, pre-processing data, feature extraction, sentiment analysis, and classification (Dandannavar, Mangalwede, & Desh, 2017).…”

Section: Data Sources and Methodologymentioning

confidence: 99%

“…The attention on sentiment analysis (SA) increased significantly from 2004 onwards, particularly on products, movie reviews, and other commercial purposes (Dandannavar, Mangalwede, & Desh, 2017). The growth of SA started to reach the government area in 2010 by collaborating with the SA agency, which helped the government monitor social media.…”

Section: Sentiment Analysismentioning

confidence: 99%

“…Using the feedback gained from social media, the SA could capture the user sentiment, whether they are happy or not, toward policy or service from the government. Moreover, as a social media analytical tool, SA will provide more insight into a new policy that is more suitable and desirable by the public and enhance the policy to be more sustainable and prosperous (Dandannavar, Mangalwede, & Desh, 2017).…”

Section: Sentiment Analysismentioning

confidence: 99%

“…After that, the data will be tokenized before applying the stop words removal. Tokenization is the process that segment the tweet into tokens by using word delimiters (Dandannavar, Mangalwede, & Desh, 2017). Since the tweet data using Bahasa Indonesia, this research loads the stop words Sastrawi library in python to remove useless words in Bahasa Indonesia.…”

Section: Pre-processingmentioning

confidence: 99%

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Evaluating Jokowi's Policy on Alcoholic Drink Investment in Indonesia by using sentiment analysis

Lestari

Coan

2022

J. Adv. Res. Soc. Sci.

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This paper discusses the evaluation of Jokowi's policy regarding alcoholic drink investment in Indonesia by using sentiment analysis. The source of data used as policy feedback in this study is data from the Twitter API. There were 6,963 tweets taken five days before and five days after Jokowi lifted the policy. The data was then manually annotated as many as 963 tweets by sentiment labels. This study uses NLP Techniques to process the data and uses SVM to classify it. Furthermore, using trained data, this paper labels the rest of the tweets into three groups of sentiments, namely positive, negative, and neutral, to see how much public support or rejection of policies on social media. From the analysis, the alcoholic drink investment policy was not successful. The number of negative sentiments from the public shows that this policy is against the people's will. Negative comments dominated during the period of this study, significantly before the President lifted the policy. Nevertheless, Jokowi's decision to revoke the alcohol investment permit is the right step and has succeeded in reducing negative comments and rejections on social media, especially Twitter. This paper also covers words that are interconnected and related in Twitter to give an overview of the tweet's contents. Before the policy was revoked, many tweets contained a rejection of Jokowi's policies, rejection of the Papuan government, and asked the MUI to suggest Jokowi revoke this policy. After the policy was revoked, the comments were more directed at criticism of the President's attitude, which stated that he hated foreign products but allowed investment in alcohol and had made policies that provoked a public controversy.

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Section: Data Sources and Methodologymentioning

confidence: 99%

Section: Sentiment Analysismentioning

confidence: 99%

Section: Sentiment Analysismentioning

confidence: 99%

Section: Pre-processingmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating Jokowi's Policy on Alcoholic Drink Investment in Indonesia by using sentiment analysis

Lestari

Coan

2022

J. Adv. Res. Soc. Sci.

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“…Individuals check out the "The most online with high reputation" when selecting a specific product prior to making a specific purchase, instead of collecting feedback from friends/relatives. Organisations can analyse their customer [10].…”

Section:  Issn: 2302-9285mentioning

confidence: 99%

Analyzing sentiment system to specify polarity by lexicon-based

Hamed¹,

Abbas

Sadiq³

2021

Bulletin EEI

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Currently, sentiment analysis into positive or negative getting more attention from the researchers. With the rapid development of the internet and social media have made people express their views and opinion publicly. Analyzing the sentiment in people views and opinion impact many fields such as services and productions that companies offer. Movie reviewer needs many processing to be prepared to detect emotion, classify them and achieve high accuracy. The difficulties arise due of the structure and grammar of the language and manage the dictionary. We present a system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus. Propose an innovative formula to compute the polarity score for each word occurring in the text and find it in positive dictionary or negative dictionary we have to remove it from text. After classification, the words are stored in a list that will be used to calculate the accuracy. The results reveal that the system achieved the best results in accuracy of 76.585%.

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Sentiment analysis of Indian PM’s “Mann Ki Baat”

Garg

2019

Int. j. inf. tecnol.

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A Proposed Framework for Evaluating the Performance of Government Initiatives Through Sentiment Analysis

Cited by 8 publications

References 7 publications

Evaluating Jokowi's Policy on Alcoholic Drink Investment in Indonesia by using sentiment analysis

Evaluating Jokowi's Policy on Alcoholic Drink Investment in Indonesia by using sentiment analysis

Analyzing sentiment system to specify polarity by lexicon-based

Sentiment analysis of Indian PM’s “Mann Ki Baat”

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