Analysis and Prediction of Temporal Twitter Popularity Using Dynamic Time Warping

Sermsai, Rattasit; Laohakiat, Sirisup

doi:10.1109/jcsse.2019.8864227

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Dapat dikatakan bahwa pada tahap ini terdapat proses mining untuk setiap kata dalam setiap tweet [10]. Setelah dilakukan beberapa tahap pre-processing, data tersebut dibagi menjadi dua, dengan bobot 70% untuk data train, dan 30% untuk data test [11]. Untuk tahap selanjutnya merupakan tahap klasifikasi dengan menggunakan Naive Bayes [12].…”

Section: Gambar 1 Alur Kerja Penelitianunclassified

Analisis Sentimen terhadap Peluang Kerja di Indonesia selama Masa Pandemi COVID-19 dengan Metode Klasifikasi Naive Bayes

Abdullah

Mahdiana

2022

mib

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The impact of the COVID-19 pandemic is very broad, one of which is in the business sector. This has resulted in an impact on job opportunities during the COVID-19 pandemic in Indonesia. This study aims to conduct in-depth learning related to job opportunities in Indonesia during the COVID-19 pandemic using the Naive Bayes model. The data source used comes from Twitter. The results of this study indicate that the largest AUC score falls to the Random Forest model (79.40%), but for more accurate precision falls to the Naive Bayes model (87.88%). In addition, there is a confusion matrix which shows that the Naive Bayes model trial is running well.

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Section: Gambar 1 Alur Kerja Penelitianunclassified

Analisis Sentimen terhadap Peluang Kerja di Indonesia selama Masa Pandemi COVID-19 dengan Metode Klasifikasi Naive Bayes

Abdullah

Mahdiana

2022

mib

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“…The authors then propose another approach in [46] to predict election results through calculating their popularity using content sentiment analysis based on tweets content terms weighting approach. In [47], the authors aim at finding tweets popularity based on time warping and sequence clustering algorithm using retweets and replies. Tweets are divided into time-based groups and then the popularity of each tweet post is calculated using sequential clustering.…”

Section: Spam and Misleading Posts Detectionmentioning

confidence: 99%

An Optimized Influencer Rating Model using a Joint Event and Theme based Approach. (c2020)

Srour¹

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The continuous development of social media platforms and exponential level of users' engagement are playing a key role in turning social media platforms into a data source that is indispensable for understanding the behavior of people. Yet this comes along with all types of challenges that needs efficient solutions and big data analysis techniques to allow capturing the different dimensions evolving over social media. Moreover, the importance of detecting influencers over social media platforms has become one of the most challenging research topics given that influencers are at the core of decision-making strategies and leading events' directions on Social Media. Generally, determining influencers can increase revenue and utility; however, measuring the influence of users is essential for determining influencers. Influencers should be credible, reliable, trustworthy, knowledgeable in the domain being discussed and have a high impact that derives people's opinions and lead them towards the proper decisions. However, influencers might play different roles when speaking about misinformation and conspiracy during sensitive and trending event. While different techniques were developed to select influencers over social networks, identifying influencers remains an evolving topic due to the dynamic nature of the social media users and use in addition to their extremely increasing growth which attracts high research interests across multiple disciplines including data science, psychology, sociology, and different human sciences all of which have been studying the topic from multiple angles. In this thesis, we further aim at identifying influencers through proposing influence rates calculation mechanism to find real and highly influential users at a certain event over Twitter using a mixed theme and event base approach with an emphasis of maximizing accuracy calculation by integrating historical influence rates in addition to content and profiles reputation. We further apply our approach on a global pandemic, the novel Coronavirus, and then we provide results and performance analysis. Finally, we conclude our work by summarizing our major findings and discussing future work.

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Machine Learning based Analysis of Influence Propagation on Social Network with Time Series Analysis

Rohini

SudalaiMuthu

2020

2020 Fourth International Conference on Inventive Systems and Control (ICISC)

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Analysis and Prediction of Temporal Twitter Popularity Using Dynamic Time Warping

Cited by 3 publications

References 8 publications

Analisis Sentimen terhadap Peluang Kerja di Indonesia selama Masa Pandemi COVID-19 dengan Metode Klasifikasi Naive Bayes

Analisis Sentimen terhadap Peluang Kerja di Indonesia selama Masa Pandemi COVID-19 dengan Metode Klasifikasi Naive Bayes

An Optimized Influencer Rating Model using a Joint Event and Theme based Approach. (c2020)

Machine Learning based Analysis of Influence Propagation on Social Network with Time Series Analysis

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