Applying Machine Learning Techniques for Sentiment Analysis in the Case Study of Indian Politics

Patil, Annapurna P.; Doshi, Dimple; Dalsaniya, Darshan; Rashmi, B. S.

doi:10.1007/978-3-319-67934-1_31

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Additionally, a variety of ML models are being used to categorize data from social media sites like Facebook and Twitter into specified categories (Patil, et. al.…”

Section: Machine Learning Applications and E-governmentmentioning

confidence: 99%

An Integration of Machine Learning with e-government: Challenges and Future Trends

Shareef

2024

ISE

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The tremendous rise of data and information and the increase in computer processing in e-governments has significantly changed how the government makes decisions relying on advanced technologies. This number will grow dramatically, and there is a prominent direction that institutions are considering the use of advanced technology to assist in delivering services and making daily decisions. The use of developed technologies, such as Machine Learning (ML), is believed appropriate in an effort to turn the massive amount of information in this extend of data into a meaningful one, and ease of analysis. Machine Learning is a technology that can handle huge data and classification for statistics or even more complex purposes, such as data analysis and decision making. The applications of ML are being used to improve the current state of e-government services by reducing processing times, lowering costs, and increasing citizen satisfaction. However, this technology still confronts a number of obstacles that obstruct its use in e-government applications, both for improving e-government systems and for increasing e-government-citizen interactions. This paper aims to address and analyse the impact of the use of ML in e-government, and also to see the feasibility of this technology in the e-services provision in developing countries, the Kurdistan Region of Iraq (KRI) in particular. In addition, it highlights the challenges that impact the integration of advanced technology with e-government and discourses on future trends. The main contribution of this paper is that it helps organizations better understand their approaches to machine learning, allowing them to make more informed decisions about how to employ developed technology in the e-government system. Peningkatan data dan informasi yang luar biasa dan peningkatan pemrosesan komputer di e-government telah secara signifikan mengubah cara pemerintah membuat keputusan dengan mengandalkan teknologi canggih. Machine Learning (ML) adalah teknologi yang dapat menangani data besar dan klasifikasi untuk statistik atau bahkan tujuan yang lebih kompleks, seperti analisis data dan pengambilan keputusan. Aplikasi ML digunakan untuk meningkatkan status layanan e-government saat ini dengan mengurangi waktu pemrosesan, menurunkan biaya, dan meningkatkan kepuasan warga. Namun, teknologi ini masih menghadapi sejumlah kendala yang menghambat penggunaannya dalam aplikasi e-government, baik untuk meningkatkan sistem e-government maupun untuk meningkatkan interaksi e-government-citizen. Makalah ini bertujuan untuk mengatasi dan menganalisis dampak penggunaan ML dalam e-government, dan juga untuk melihat kelayakan teknologi ini dalam penyediaan layanan elektronik di negara-negara berkembang, Wilayah Kurdistan Irak (KRI) pada khususnya. Selain itu, menyoroti tantangan yang berdampak pada integrasi teknologi canggih dengan e-government dan wacana tentang tren masa depan. Kontribusi utama dari makalah ini adalah membantu organisasi lebih memahami pendekatan mereka terhadap pembelajaran mesin, memungkinkan mereka untuk membuat keputusan yang lebih tepat tentang bagaimana menggunakan teknologi yang dikembangkan dalam sistem e-government.

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“…Additionally, a variety of ML models are being used to categorize data from social media sites like Facebook and Twitter into specified categories (Patil, et. al.…”

Section: Machine Learning Applications and E-governmentmentioning

confidence: 99%

An Integration of Machine Learning with e-government: Challenges and Future Trends

Shareef

2024

ISE

View full text Add to dashboard Cite

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“…Also, doctors could adapt their prescriptions, schedules, and practices, even pharmaceutical companies benefit by analyzing the public effect of medications and planning studies based on patient attitudes. Moreover, politicians use posts and comments on social media and news articles to determine people's political orientation [4], predict election results [5], and gauge public opinion about changes in legislation or policy projects. In conjunction with the expansion of digital marketing [6], business entities invest in the study of customer perceptions and preferences [7], by analyzing shared opinions about their products and services.…”

Section: Introductionmentioning

confidence: 99%

MSTD: Moroccan Sentiment Twitter Dataset

Mihi¹,

Ait²,

El³

et al. 2020

IJACSA

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With the proliferation of social media and Internet accessibility, a massive amount of data has been produced. In most cases, the textual data available through the web comes mainly from people expressing their views in informal words. The Arabic language is one of the hardest Semitic languages to deal with because of its complex morphology. In this paper, a new contribution to the Arabic resources is presented as a large Moroccan dataset retrieved from Twitter and carefully annotated by native speakers. For the best of our knowledge, this dataset is the largest Moroccan dataset for sentiment analysis. It is distinguished by its size, its quality given by the commitment of annotators, and its accessibility for the research community. Furthermore, the MSTD (Moroccan Sentiment Twitter Dataset) is benchmarked through experiments carried out for 4-way classification as well as polarity classification (positive, negative). Various machine-learning algorithms are combined to feature extraction techniques to reach optimal settings. This work also presents the effect of stemming and lemmatization on the improvement of the obtained accuracies.

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“…All that data has functioned as a fuel for data exploration and data-based predictive models, giving rise to better and more complex models as well as new and innovative use cases [1], [2]. Such cases of Machine Learning algorithms together with data science and analysis methods have initiated changes across many industrial aspects, resulting in developing novel business approaches in transportation [3], healthcare [4], education [5], production and political campaigns, referendums and governments [1], [6].…”

Section: Introductionmentioning

confidence: 99%

Data Science Methods and Machine Learning Algorithm Implementations for Customized Pratical Usage

Mrzic¹,

Zaimović²

2020

TEM Journal

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Due to the unprecedented rise of data content over the last decade, an opportunity for databased personalization and analysis has become a norm in the modern world. By implementing Machine Learning algorithms and Data Science methods no industry remained unchanged. This paper applied these methods and algorithms in personal, practical examples in order to see their benefits in our day-today lives. For the purpose of this case study, we analyzed three cases: a personal movie recommender, messages analysis and real estate trends and predictions on the local market. For this research we used global and personal data, and applied a suitable machine learning model. The purpose of this paper is to establish how one individual, and in what measure, with the use of these new technological tools, can ease his decision making process and manage a more tailored lifestyle.

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Applying Machine Learning Techniques for Sentiment Analysis in the Case Study of Indian Politics

Cited by 5 publications

References 4 publications

An Integration of Machine Learning with e-government: Challenges and Future Trends

An Integration of Machine Learning with e-government: Challenges and Future Trends

MSTD: Moroccan Sentiment Twitter Dataset

Data Science Methods and Machine Learning Algorithm Implementations for Customized Pratical Usage

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