Twitter Sentiment Analysis on Indian Government Project using R.

Verma, Pankaj; Khanday, Akib Mohi Ud Din; Rabani, Syed Tanzeel; Hussain, M. Mahmood; Jamwal, Sanjay

doi:10.35940/ijrte.c6612.098319

Cited by 22 publications

(13 citation statements)

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“…R is a programming language and free software environment for statistical computing and graphics supported by the R foundation for statistical computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis [19]. It is able to download, plot, analyze the data taken on Twitter, while relation between R, and Twitter applies protocol, open authorization (Oauth) [20].…”

Section: Research Methods 21 Connecting R Programing To Twittermentioning

confidence: 99%

Data mining applied about polygamy using sentiment analysis on Twitters in Indonesian perception

et al. 2021

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Polygamy remains one of the interesting key topics in various society, especially Indonesia. Polygamy is the act of marrying multiple spouses that means having more one wife at the same time, is common case in worldwide. In this problem, the most of Muslim in Indonesia adopt Islamic law that let men to do the polygamy with certain requirements. In worldwide communities, this has been a prominent feature and has become the subject of numerous books, heated debates, journal articles, discussion papers, and so on. Furthermore, in Indonesia the polygamous marriage has recently become a heated topic. Despite controversy, Indonesia’s law, of recent, allows people for doing polygamy in certain conditions. Because the polygamy is often debated, this study focuses on assessment of Indonesian’s perceptions through sentiment analysis and would determine people’s perception about polygamy issue from 500 tweets on Twitter. To conclude, it elucidates that the polygamy in Indonesia is a normal thing and few assume the case is as negative thing.

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Section: Research Methods 21 Connecting R Programing To Twittermentioning

confidence: 99%

Data mining applied about polygamy using sentiment analysis on Twitters in Indonesian perception

et al. 2021

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“…Users can post on a wide variety of issues and it is a space from which enables data extraction on public opinion using linguistic programming models. Then, Machine learning (ML) techniques are commonly used to determine if a message posted by an user expresses a positive or negative sentiment [38,43,51]. These methods of analysis are known as sentiment or sentimental analysis (SA).…”

Section: Related Workmentioning

confidence: 99%

“…Presidential campaign prediction using Twitter data is a much-discussed research topic and the number of studies are still growing [9,10,24,27,37,51]. There is, however, no real benchmarking on the performance [22] to identify the best method to determine the winner of the elections or the voting share with the minimum error.…”

Section: Algorithms Comparison With State Of Artmentioning

confidence: 99%

Inferring the votes in a new political landscape: the case of the 2019 Spanish Presidential elections

2020

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For more than a decade now, with the emergence of Internet 2.0, users have been able to generate their own content and share it publicly with relative ease. This boom has witnessed a huge surge in the popularity of Online Social Networks (OSN), in particular the Twitter microblogging platform which allows its users to share text messages of up to 140 characters with their family, friends and followers [12]. More than 500 million messages, commonly known as tweets, are posted every day [20, 34]. Given that the main reason behind these publications is to express the opinion of the users, they have proven to be great interest to be analysed [14, 38, 48]. This huge amount of content circulating in OSN has attracted the attention of marketing agencies that seek capitalize on the behaviour of clients (current or future) to adjust their online campaigns and even use the social content (number of likes, retweets, etc.) to predict consumer behaviour in the real world [45, 53]. Recently this type of analysis

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“…Users can post on a wide variety of issues and it is a space from which enables data extraction on public opinion using linguistic programming models. Then, Machine Learning (ML) techniques are commonly used to determine if a message posted by an user expresses a positive or negative sentiment (Mehta et al, 2020;Patel & Chhinkaniwala, 2019;Verma et al, 2019). These methods of analysis are known as Sentiment or Sentimental Analysis (SA).…”

Section: Related Workmentioning

confidence: 99%

“…Presidential campaign prediction using Twitter data is a much-discussed research topic and the number of studies are still growing (Awais et al, 2019;Bansal & Srivastava, 2019;Heredia et al, 2018;Jaidka et al, 2019;McGregor et al, 2017;Verma et al, 2019). There is, however, no real benchmarking on the performance (Gayo-Avello, 2013) to identify the best method to determine the winner of the elections or the voting share with the minimum error.…”

Section: Algorithms Comparison With State Of Artmentioning

confidence: 99%

Inferring the votes in a new political landscape. The case of the 2019 Spanish Presidential elections.

Grimaldi

Díaz

Arboleda

2020

Preprint

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Abstract The avalanche of personal and social data circulating in Online Social Networks over the past 10 years has attracted a great deal of interest from Scholars and Practitioners who seek to analyse not only their value, but also their limits. Predicting election results using Twitter data is an example of how data can directly influence the politic domain and it also serves an appealing research topic. This article aims to predict the results of the 2019 Spanish Presidential election and the voting share of each candidate, using Tweeter. The method combines sentiment analysis and volume information and compares the performance of five Machine Learning algorithms. Several data scrutiny uncertainties arose that hindered the prediction of the outcome. Consequently, the method develops a political lexicon-based framework to measure the sentiments of online users. Indeed, an accurate understanding of the contextual content of the tweets posted was vital in this work. Our results correctly ranked the candidates and determined the winner by means of a better prediction of votes than official research institutes.

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Twitter Sentiment Analysis on Indian Government Project using R.

Cited by 22 publications

References 1 publication

Data mining applied about polygamy using sentiment analysis on Twitters in Indonesian perception

Data mining applied about polygamy using sentiment analysis on Twitters in Indonesian perception

Inferring the votes in a new political landscape: the case of the 2019 Spanish Presidential elections

Inferring the votes in a new political landscape. The case of the 2019 Spanish Presidential elections.

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