SVM and k-Means Hybrid Method for Textual Data Sentiment Analysis

Korovkinas, Konstantinas; Danėnas, Paulius; Garšva, Gintautas

doi:10.22364/bjmc.2019.7.1.04

Cited by 26 publications

(7 citation statements)

References 30 publications

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“…The accuracy obtained for their proposed method, on their dataset, which is French articles obtained from national and international newspapers, is around 91%. So far, many works have been done using Support Vector Machine and Naïve Bayes algorithm for sentiment analysis [15][16][17][18][19]. The first work that has been done in the field of sentiment analysis in Persian language is the use of two standard methods of Support Vector Machine and Naïve Bayes in the field of movie review [20].…”

Section: Machine Learning Based Methodsmentioning

confidence: 99%

A Hybrid Approach to Sentiment Analysis of Iranian Stock Market User’s Opinions

Ahangari

Sebti

2023

IJE

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With the significant growth of social media, individuals and organizations are increasingly using public opinion in these media to make their own decisions. The purpose of sentiment analysis is to automatically extract people's sentiments from those social networks. Social networks related to financial markets, including stock markets, have recently attracted the attention of many individuals and organizations. people in these networks share their opinions and ideas about each share in the form of a post or tweet. In fact, sentiment analysis in this field is the assessment of people's attitude towards each share. There are different approaches in sentiment analysis, in this article, a hybrid approach is proposed for sentiment analysis. In this way the feature vector used in machine learning is obtained from a lexicon that is automatically extracted from user's tweets. This lexicon is made by using stock price information related to user's opinion. Also, by using the next day's price information of each share, amendments were suggested to this lexicon. Therefore, the lexicon generated for the feature vector was constructed in three ways, and all three methods reported about an 8% improvement over the baseline method in terms of Fscore. The baseline method that is considered for this work, is the Persian version of SentiStrength lexicon which is designed for general purpose.

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Section: Machine Learning Based Methodsmentioning

confidence: 99%

A Hybrid Approach to Sentiment Analysis of Iranian Stock Market User’s Opinions

Ahangari

Sebti

2023

IJE

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“…The first thing to do in K-means clustering is assigning the number of clustering, k. After that, initially, the random centroid for k cluster is chosen. The iteration of K-Means is done until the mean of each training data to the centroid met the stopping criterion, whereas the smallest Euclidean distance from a sample is the nearest centroid for the sample to be the one with [22], [23].…”

Section: K-means Clusteringmentioning

confidence: 99%

Text mining approaches for analyzing an Indonesian tafseer and translation of the holy Quran

Ayu

Irawan

Mantoro

2022

IJEECS

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<span lang="EN-US">The Indonesian tafseer and translation of Holy Quran is an important source of information and knowledge for Indonesian muslims, since not many Indonesian muslims understand Arabic language in the Quran. However, the tafseer is full of the commentaries and explanation of each surah (chapter) and/or ayah (verse), which form a large document and not so easy to be accessed. Thus, the challenge is how to refer to both tafseer and translation in faster and accurate ways as one needs to always refer to them back and forth. Hence, this study proposes several text mining approaches, i.e. most frequent words, K-means clustering, and association rules, to analyze an Indonesian tafseer and translation of Quran and provide insights of hidden knowledge and relationships based on statistical information derived from it. These insights could be useful for muslims in general and for people that doing research in related areas. This study shows interesting results from combined analysis of the approaches used which can help people accessing information in tafseer more efficiently. As well, interesting relationships have been drawn from terms in the tafseer which could provide further and deeper knowledge on messages in the Quran.</span>

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“…The clustering process maximizes the performance of the SVM classifier. Also, (Korovkinas et al, 2019; Liu & Lee, 2018a) apply the k‐means and SVM classifier to classify the email data's sentiment. However, (Liu & Lee, 2018b), use the k‐means as a data labelling approach.…”

Section: Related Workmentioning

confidence: 99%

Sentiment analysis from email pattern using feature selection algorithm

Srinivasarao

Sharaff

2021

Expert Systems

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Today number of applications are available on mobile devices and computers for electronic mail (email) conversations. The demand for email communication is increasing day‐by‐day. Therefore the incoming and outgoing messages are also getting increased. However, extracting the sentiments from the emails is now demanding. Therefore in the proposed method, the pattern classification and sentiment clustering are carried out in two phases. Initially, the pattern classification is performed using support vector regression, then the sentiments from such classified patterns are clustered using a unsupervised fuzzy‐model‐based Gaussian clustering algorithm. Finally, the experimental analysis is performed in Python tool. The proposed sentiment clustering from email patterns has attained a better accuracy result of 97.13%, which is found higher than other existing techniques. Along with the parametric analysis, non‐parametric statistical analysis using the Wilcoxon rank‐sum test is also carried out to identify the proposed sentiment analysis architecture's effectiveness.

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SVM and k-Means Hybrid Method for Textual Data Sentiment Analysis

Cited by 26 publications

References 30 publications

A Hybrid Approach to Sentiment Analysis of Iranian Stock Market User’s Opinions

A Hybrid Approach to Sentiment Analysis of Iranian Stock Market User’s Opinions

Text mining approaches for analyzing an Indonesian tafseer and translation of the holy Quran

Sentiment analysis from email pattern using feature selection algorithm

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