Microblog Sentiment Classification Using Parallel SVM in Apache Spark

Yan, Bo; Yang, Zijiang; Ren, Yitian; Tan, Xing; Liu, Eric

doi:10.1109/bigdatacongress.2017.43

Cited by 17 publications

(8 citation statements)

References 16 publications

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“…The application of SVM in text classification or SA has been successfully carried out in many studies. A recent study used the in-memory framework Apache Spark to apply a SA by using an SVM with a rbf kernel to classify microblog comments (Yan, Yang, Ren, Tan, & Liu, 2017). Al-Smadi, Qawasmeh, Al-Ayyoub, Jararweh, and Gupta (2018) compared the performance of recurrent neural networks (RNN) and SVMs on a comprehensive aspect-based SA of Arabic hotel ratings.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Opinion-based Homogeneity on YouTube: Combining Sentiment and Social Network Analysis

Röchert¹,

Neubaum²,

Roß³

et al. 2019

Preprint

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When addressing public concerns such as the existence of politically like-minded communication spaces in social media, analyses of complex political discourses are met with increasing methodological challenges to process communication data properly. To address the extent of political like-mindedness in online communication, we argue that it is necessary to focus not only on ideological homogeneity in online environments, but also on the extent to which specific political questions are discussed in a uniform manner. This study proposes an innovative combination of computational methods, including natural language processing and social network analysis, that serves as a model for future research examining the evolution of opinion climates in online networks. Data were gathered on YouTube, enabling the assessment of users’ expressed opinions on three political issues (i.e., adoption rights for same-sex couples, headscarf rights, and climate change). Challenging widely held assumptions on discursive homogeneity online, the results provide evidence for a moderate level of connections between dissimilar YouTube comments but few connections between agreeing comments. The findings are discussed in light of current computational communication research and the vigorous debate on the prevalence of like-mindedness in online networks.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Opinion-based Homogeneity on YouTube: Combining Sentiment and Social Network Analysis

Röchert¹,

Neubaum²,

Roß³

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…ANN is another one of the most popular methods used in machine learning. It shows very good results in researching many different scenarios, for example, in social media analysis (Yan, 2017), image processing (Jifara, Jiang, Rho, Cheng, & Liu, 2019) or marketing efficiency (Salminen, Yoganathan, Corporan, Jansen, & Jung, 2019). The indisputable advantage of this method is its effectiveness in comparison with other methods that we have investigated.…”

Section: Artificial Neural Networkmentioning

confidence: 91%

An evaluation of machine learning and latent semantic analysis in text sentiment classification

Miazga

Hachaj

2020

Technical Transactions

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In this paper, we compare the following machine learning methods as classifiers for sentiment analysis: k – nearest neighbours (kNN), artificial neural network (ANN), support vector machine (SVM), random forest. We used a dataset containing 5,000 movie reviews in which 2,500 were marked as positive and 2,500 as negative. We chose 5,189 words which have an influence on sentence sentiment. The dataset was prepared using a term document matrix (TDM) and classical multidimensional scaling (MDS). This is the first time that TDM and MDS have been used to choose the characteristics of text in sentiment analysis. In this case, we decided to examine different indicators of the specific classifier, such as kernel type for SVM and neighbour count in kNN. All calculations were performed in the R language, in the program R Studio v 3.5.2. Our work can be reproduced because all of our data sets and source code are public.

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“…Moreover, in the blockchain, and especially in the cryptocurrency networks, the authenticity of data is frequently verified by an asymmetric encryption technology known as public-key cryptography (PKC) [ 45 ]. In this technology, both the transmitter and receiver have a pair of keys consisting of a public key and a private one [ 46 ]. The private key is exclusively accessible to the nodes that created it, whereas the public key is spread rather freely throughout the network.…”

Section: Blockchain Architecturementioning

confidence: 99%

Blockchain for Modern Applications: A Survey

Krichen

Ammi

Mihoub

et al. 2022

Sensors

106

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Blockchain is a modern technology that has revolutionized the way society interacts and trades. It could be defined as a chain of blocks that stores information with digital signatures in a distributed and decentralized network. This technique was first adopted for the creation of digital cryptocurrencies, such as Bitcoin and Ethereum. However, research and industrial studies have recently focused on the opportunities that blockchain provides in various other application domains to take advantage of the main features of this technology, such as: decentralization, persistency, anonymity, and auditability. This paper reviews the use of blockchain in several interesting fields, namely: finance, healthcare, information systems, wireless networks, Internet of Things, smart grids, governmental services, and military/defense. In addition, our paper identifies the challenges to overcome, to guarantee better use of this technology.

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Microblog Sentiment Classification Using Parallel SVM in Apache Spark

Cited by 17 publications

References 16 publications

Opinion-based Homogeneity on YouTube: Combining Sentiment and Social Network Analysis

Opinion-based Homogeneity on YouTube: Combining Sentiment and Social Network Analysis

An evaluation of machine learning and latent semantic analysis in text sentiment classification

Blockchain for Modern Applications: A Survey

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