Casting online votes: To predict offline results using sentiment analysis by machine learning classifiers

Juneja, P.; Ojha, Uma

doi:10.1109/icccnt.2017.8203996

Cited by 18 publications

(10 citation statements)

References 8 publications

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“…[25] have shown that SVM with Normalized Poly Kernel achieved the best performance compared to other kernels. However, in other studies [9] [17], SVM with ordinary kernels, linear and RBF kernel, showed less effective performance than other algorithms. It is because a key success factor in SVM is the choice of kernel function.…”

Section: Sentiment Of the Economic And General Aspects In World Cloudmentioning

confidence: 86%

Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

Prastyo

Sumi

Dian

et al. 2020

JISEBI

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Background: Handling COVID-19 (Corona Virus Disease-2019) in Indonesia was once trending on Twitter. The Indonesian government's handling evoked pros and cons in the community. Public opinions on Twitter can be used as a decision support system in making appropriate policies to evaluate government performance. A sentiment analysis method can be used to analyse public opinion on Twitter.Objective: This study aims to understand public opinion trends on COVID-19 in Indonesia both from a general perspective and an economic perspective.Methods: We used tweets from Twitterscraper library. Because they did not have a label, we provided labels using sentistrength_id and experts to be classified into positive, negative, and neutral sentiments. Then, we carried out a pre-processing to eliminate duplicate and irrelevant data. Next, we employed machine learning to predict the sentiments for new data. After that, the machine learning algorithms were evaluated using confusion matrix and K-fold cross-validation.Results: The SVM analysis on the sentiments on general aspects using two-classes dataset achieved the highest performance in average accuracy, precision, recall, and f-measure with the value of 82.00%, 82.24%, 82.01%, and 81.84%, respectively.Conclusion: From the economic perspective, people seemed to agree with the government’s policies in dealing with COVID-19; but people were not satisfied with the government performance in general. The SVM algorithm with the Normalized Poly Kernel can be used as an intelligent algorithm to predict sentiment on Twitter for new data quickly and accurately.

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Section: Sentiment Of the Economic And General Aspects In World Cloudmentioning

confidence: 86%

Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

Prastyo

Sumi

Dian

et al. 2020

JISEBI

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“…CRFs, used by 4 studies [228,238,200,199], are a type of discriminative classifier that model the decision boundary amongst different classes, whereas LiR was also used by 4 studies [194,241,232,239]. Moreover, 3 studies each used the SANT [78,75,239] and SGD [227,244,240] algorithms, with the former being mostly used for comparison purposes to the proposed approaches by the respective authors.…”

Section: Algorithm Number Of Studies Referencementioning

confidence: 99%

“…The Politics domain is the dominant application area with 45 studies applying social opinion mining on different events, namely elections [453,103,197,50,121,87,88,157,53,327,244,539,421,422,337,203,168,368,442,222,520,178,212,184,117,511,190,441,310,406,82], reforms, such as equality marriage [130], debates [180], referendums [241,540], political parties or politicians [60,111,466], and political events, such as terrorism, protests, uprisings and riots [420,437,330,556,205,248,188].…”

Section: Application Areas Of Social Opinion Miningmentioning

confidence: 99%

Over a Decade of Social Opinion Mining: A Systematic Review

Cortis,

Davis

2020

Preprint

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Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing and other

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“…Trabajos publicados en relación a algoritmos de aprendizaje de máquina, análisis de sentimientos y redes sociales fueron publicados en [16] y [17], donde se clasifican los datos de Twitter en polaridad de sentimientos utilizando diferentes clasificadores de aprendizaje supervisado. Se concluye que los resultados son buenos, aunque aún hay problemas no triviales como el sesgo de la muestra y la comprensión automática del contenido textual.…”

Section: Análisis De Sentimientos Y Otras Técnicas De Predicciónunclassified

Predicción electoral usando un modelo híbrido basado en análisis sentimental y seguimiento a encuestas: elecciones presidenciales de Colombia

Callejas-Cuervo

Vélez-Guerrero

2019

Rev. politec.

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La disponibilidad de los medios digitales ha proporcionado una poderosa herramienta para expresar opiniones incluyendo aspectos sociales y políticos desarrollados en cada región. En Colombia, el uso de redes sociales ha dado lugar a la difusión masiva de opiniones políticas, especialmente durante el período de campaña en las elecciones presidenciales nacionales. Este trabajo propone un modelo híbrido para predecir el desenlace de la primera vuelta en las elecciones presidenciales de Colombia en 2018 (pre-hoc), cuyo objetivo es minimizar el error absoluto y mejorar la calidad de la predicción final. Las actividades de los usuarios en Twitter y Facebook fueron registradas y analizadas, obteniendo como resultado una predicción precisa y coherente con la realidad, donde el RMSE del modelo híbrido ronda el 2,47%, superando en promedio el RMSE de las firmas encuestadoras tradicionales más prominentes del país. Adicionalmente también se predijo el valor del abstencionismo electoral con un error diferencial de 1,72% con respecto al valor real, demostrando la confiabilidad de la metodología propuesta.

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Casting online votes: To predict offline results using sentiment analysis by machine learning classifiers

Cited by 18 publications

References 8 publications

Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

Over a Decade of Social Opinion Mining: A Systematic Review

Predicción electoral usando un modelo híbrido basado en análisis sentimental y seguimiento a encuestas: elecciones presidenciales de Colombia

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