SVM-based sentiment classification: a comparative study against state-of-the-art classifiers

Sotiropoulos, Dionisios N.; Pournarakis, Demitrios E.; Giaglis, George M.

doi:10.1504/ijcistudies.2017.10007054

Cited by 3 publications

(5 citation statements)

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“…The hyperplane is a one-dimensional subspace that smaller than the surrounding space and is used to separate data when there are three dimensions or more [16]. SVM is a nonlinear classification algorithm that operates in a vector space whose dimensions are larger than the original feature space of the given dataset [3]. Therefore, SVM provides a kernel function feature that consists of linear, polynomial, RBF, and sigmoid [17].…”

Section: Classification With Support Vector Machine (Svm)mentioning

confidence: 99%

“…For this reason, sentiment analysis can be done as an attempt to conduct the survey without having to interact directly with alumni. Sentiment analysis is one of the important areas of research in social media analysis because it concentrates on detecting the polarity of opinions or emotions from texts on social media [3].…”

Section: Introductionmentioning

confidence: 99%

“…In this study, the sentiment analysis model was built to classify the polarity of discussions from alumni on the Telkom University Alumni Forum (FAST) Facebook group using the Support Vector Machine (SVM) method because the method proved efficient in overcoming the problem of imbalanced data in sentiment analysis [3]. In addition, based on previous studies, the SVM algorithm can produce the highest confusion matrix value compared to other classification algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Sentiment and Discussion Topic Analysis on Social Media Group using Support Vector Machine

Adityani

Richasdy

Astuti

2022

mib

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The growth of social media in this modern era is increasingly rapid, where people are very active digitally interacting with each other. People who have a common interest or simply like to be in a community often gather in an online group, especially on Facebook. Alumni of Telkom University are no exception, who are also actively discussing and sharing information in Telkom University Alumni Forum Facebook group (FAST). By using their status from that group, sentiment and topic discussion analysis can be performed to determine whether the polarity is positive, neutral, or negative. In Addition, topic modeling extracts what topics are often discussed in the group. In this research, sentiment analysis was performed using the Support Vector Machine (SVM) method. Also, the classification process involved TF-IDF for word weighting and confusion matrix as performance measurement. Several testing scenarios were carried out to get the best accuracy value. Based on the tests performed on the preprocessing technique and feature extraction n-gram addition, the highest accuracy value obtained is 80.56%. The result indicates that the best performance is obtained by combining preprocessing techniques without the stopword removal process and feature extraction unigram. Moreover, the topics discussed based on topic modeling results were related to telecommunication and Telkom, Indonesia, alumni, and FAST.

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Section: Classification With Support Vector Machine (Svm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sentiment and Discussion Topic Analysis on Social Media Group using Support Vector Machine

Adityani

Richasdy

Astuti

2022

mib

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“…SVMs has also been applied in the domain of text mining as it shows satisfactory results. The high dimensionality of text data makes SVMs a useful algorithm to apply and also avoids the curse of dimensionality problem of text data [39]. An implementation of SVMs named SMO algorithm [40] has been applied to the dataset, which is an open-source implementation of SVMs.…”

Section: ) Support Vector Machinementioning

confidence: 99%

Machine Learning Approaches for Predicting the Severity Level of Software Bug Reports in Closed Source Projects

Baarah¹,

Aloqaily²,

Salah³

et al. 2019

IJACSA

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In Software Development Life Cycle, fixing defect bugs is one of the essential activities of the software maintenance phase. Bug severity indicates how major or minor the bug impacts on the execution of the system and how rapidly the developer should fix it. Triaging a vast amount of new bugs submitted to the software bug repositories is a cumbersome and time-consuming process. Manual triage might lead to a mistake in assigning the appropriate severity level for each bug. As a consequence, a delay for fixing severe software bugs will take place. However, the whole process of assigning the severity level for bug reports should be automated. In this paper, we aim to build prediction models that will be utilized to determine the class of the severity (severe or non-severe) of the reported bug. To validate our approach, we have constructed a dataset from historical bug reports stored in JIRA bug tracking system. These bug reports are related to different closed-source projects developed by INTIX Company located in Amman, Jordan. We compare eight popular machine learning algorithms, namely Naive Bayes, Naive Bayes Multinomial, Support Vector Machine, Decision Tree (J48), Random Forest, Logistic Model Trees, Decision Rules (JRip) and K-Nearest Neighbor in terms of accuracy, F-measure and Area Under the Curve (AUC). According to the experimental results, a Decision Tree algorithm called Logistic Model Trees achieved better performance compared to other machine learning algorithms in terms of Accuracy, AUC and F-measure with values of 86.31, 0.90 and 0.91, respectively.

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“…In order to get the optimal solution for sentiment analysis, they performed the experiments on three datasets obtained from IMDB movie reviews and Amazon. In the same way for sentiment classification, Sotiropoulos et al (2017) did a deep comparison of SVM classifier with other state-of-the-art machine learning classifiers. Experiments were done on a benchmark dataset collected from Greek bank sector.…”

Section: Introductionmentioning

confidence: 99%

Identification and characterization of Choroidal Neovascularization (CNV) using eHealth data through an optimal classifier

Anitha

Ismail

Lakshmanaprabu

2020

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Over the years, health informatics and eHealth gained more popularity in health care application. The collection of eHealth data becomes easier due to the advancement of digital technology. In this paper, the e-Health based supporting system is developed for the classification of a retinal disease called CNV. CNV is a retinal disease caused due to the growth of abnormal blood vessels in the choroidal layer. A good classifier for CNV data makes the process of identifying the disease easier and it will help the medical practitioners to give the treatment at the right time. A comparison has been done among different machine learning classifiers such as support vector machine (SVM), k-nearest neighbours (kNN), neural network (NN), ensemble and naive Bayes classifiers and they are tested and evaluated based on accuracy and training time. From the results, it is observed that kNN classifier outperforms the other classifiers in all aspects.

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SVM-based sentiment classification: a comparative study against state-of-the-art classifiers

Cited by 3 publications

References 0 publications

Sentiment and Discussion Topic Analysis on Social Media Group using Support Vector Machine

Sentiment and Discussion Topic Analysis on Social Media Group using Support Vector Machine

Machine Learning Approaches for Predicting the Severity Level of Software Bug Reports in Closed Source Projects

Identification and characterization of Choroidal Neovascularization (CNV) using eHealth data through an optimal classifier

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