Peringkasan Sentimen Esktraktif di Twitter Menggunakan Hybrid TF-IDF dan Cosine Similarity

Wahid, Devid Haryalesmana; Sn, Azhari

doi:10.22146/ijccs.16625

Cited by 75 publications

(45 citation statements)

References 0 publications

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“…-5 (memiliki sentimen negatif). Boosterwords adalah kata yang dapat meningkatkan atau mengurangi intensitas sentimen kata disebelahnya [19]. Kata ini diberi bobot 1-2 untuk menambah atau mengurangi skor kata disampingnya.…”

Section: Sentiment Scoringunclassified

Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (Svm

Fitriyah

Warsito

Maruddani

2020

J.Gauss

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Appearance of PT Aplikasi Karya Anak Bangsa or as known as Gojek since 2015 give a convenience facility to people in Indonesia especially in daily activities. Sentiment analysis on Twitter social media can be the option to see how Gojek users respond to the services that have been provided. The response was classified into positive sentiment and negative sentiment using Support Vector Machine method with model evaluation 10-fold cross validation. The kernel used is the linear kernel and the RBF kernel. Data labeling can be done with manually and sentiment scoring. The test results showed that the RBF kernel gets overall accuracy and the highest kappa accuracy on manual data labeling and sentiment scoring. On manual data labeling, the overall accuracy is 79.19% and kappa accuracy is 16.52%. While the labeling of data with sentiment scoring obtained overall accuracy of 79.19% and kappa accuracy of 21%. The greater overall accuracy value and kappa accuracy obtained, the better performance of the classification model. Keywords: Gojek, Twitter, Support Vector Machine, overall accuracy, kappa accuracy

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Section: Sentiment Scoringunclassified

Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (Svm

Fitriyah

Warsito

Maruddani

2020

J.Gauss

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“…Ada hubungan positif dan signifikan antara perilaku pelaku cyberbullying dan perilaku korban cyberbullying yang mana semakin reaktif perilaku pelaku cyberbullying maka semakin reaktif pula perilaku korban cyberbullying. Hal ini membuktikan betapa besarnya pengaruh cyberbullying terhadap kehidupan sosial [2]. Analisis sentimen merupakan sub bagian dari Natural Language Processing (NLP) yang fokus pada menentukan perasaan yang terkandung pada sebuah teks.…”

Section: Pendahuluanunclassified

Sentiment Analysis Of Cyberbullying On Twitter Using SentiStrength

Khaira

Johanda

Utomo

et al. 2020

IJAIDM

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Cyberbullying is a form of bullying that takes place across virtually every social media platform. Twitter is a form of social media that allows users to exchange information. Bullying has been a growing problem on Twitter over the past few years. Sentiment analysis is done to identify the element of bullying in a tweet. Sentiments are divided into 3 classes, namely Bullying, Non-Bullying and neutral. There are three steps to classify cyberbullying i.e. collection of data set, preprocessing data, and classification process. This research used sentiStrength, an algorithm which uses a lexicon based approach. This SentiStrength lexicon contains the weight of its sentiment strength. The assessment results from 454 tweets data obtained 161 tweet non-bullying (35.4%), 87 tweet neutral (19.1%), and 206 tweet bullying (45.4%). This research produces an accuracy value of 60.5%.

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“…Melakukan penelitian Peringkasan Sentimen Esktraktif di Twitter Menggunakan Hybrid TF-IDF dan Cosine Similarity. Penelitian tersebut bertujuan untuk mengekstraksi sentimen masyarakat tentang topik selebriti di Twitter secara otomatis dengan studi kasus Agnes Monica menggunakan kombinasikan metode SentiStrength, Hybrid TF-IDF dan Cosine Similarity [12].…”

Section: Pendahuluanunclassified

“…:0.42, interpersonal:0.63, intrapersonal:0.42}. Maka didapatkan nilai tertinggi dari opini kalimat tersebut adalah +5 [12]. G. Penentuan tipe kecerdasan kalimat Nilai terbesar dari setiap tipe kecerdasan yang telah diatributkan pada kalimat menjadi tipe kecerdasan kalimat.…”

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Klasifikasi Kecerdasan Majemuk pada Anak Berdasarkan Posting Aktivitas di Media Sosial Menggunakan SentiStrength dan Spearman's Rank Correlation Coefficient

Siregar

Aprilia

Anggaraeni

et al. 2019

mib

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Intelligence are things related to intelligence, intellectual action, and perfect development of the mind. Every human being has the right to develop himself based on intelligence. A child who is good at playing violin shouldn't let himself feel like he is stupid because he is unable to complete his mathematical tasks. Therefore, the author wants to create a system for determining children's talent based on multiple intelligence as measured by the tendency of posts about their daily activities on social media by using the Sentistrength and Spearman's Correlation Coefficient methods. The purpose of this research is to create a social media application called Juicer, which is able to determine one's talent according to multiple intelligence theory based on data in the form of user posts about their daily activities. Users input their daily activities and opinions about the activities that he is currently undergoing. The system will analyze the data entered a negative or positive opinion. Then determine whether the data entered are musical-rhythmic, visual-spatial, verbal-linguistic, logical-mathematical, bodily-kinesthetic, interpersonal, intrapersonal, naturalistic, and / or spiritual-type using lexicon-based sentiment analysis with the Sentistrength method. After a long period of time, the system will sort the data that has been stored. After the data is classified, user intelligence types will be found based on multiple intelligence theory, as a result of this study. The result of the correlation between intelligence type data obtained through the system compared to intelligence type data obtained through the manual method is 80%, and the deviation value is 0.09.

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Peringkasan Sentimen Esktraktif di Twitter Menggunakan Hybrid TF-IDF dan Cosine Similarity

Abstract: Abstrak

Cited by 75 publications

References 0 publications

Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (Svm

Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (Svm

Sentiment Analysis Of Cyberbullying On Twitter Using SentiStrength

Klasifikasi Kecerdasan Majemuk pada Anak Berdasarkan Posting Aktivitas di Media Sosial Menggunakan SentiStrength dan Spearman's Rank Correlation Coefficient

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