scholarly journals Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM)

2020 ◽  
Vol 4 (3) ◽  
pp. 650
Author(s):  
Rian Tineges ◽  
Agung Triayudi ◽  
Ira Diana Sholihati
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Classification Model ◽  
Support Vector ◽  
Internet Service ◽  
Active User ◽  
The Social ◽  
Twitter Users ◽  
Use Of The Internet

In the year 2018, 18.9% of the population in Indonesia mentioned that the main reason for their use of the Internet is social media. One of the social media with an active user of 6.43 million users is Twitter. Based on the surge of information published via Twitter, it is possible that such information may contain the user's opinions on an object, such objects may be events around the community such as a product or service. This makes the company use Twitter as a medium to disseminate information. An example is an Internet Service Provider (ISP) such as Indihome. Through Twitter, users can discuss each other's complaints or satisfaction with Indihome's services. It takes a method of sentiment analysis to understand whether the textual data includes negative opinions or positive opinions. Thus, the authors use the Support Vector Machine (SVM) method in sentiment analysis on the opinions of the Indihome service user on Twitter, with the aim of obtaining a sentiment classification model using SVM, and to know how much accuracy the SVM method generates, which is applied to sentiment analysis, and to see how satisfied the Indihome service users are based on Twitter. After testing with SVM method The result is accuracy 87%, precision 86%, recall 95%, error rate 13%, and F1-score 90%

scholarly journals Sentiment Analysis with Machine Learning Methods on Social Media

2020 ◽  
Vol 9 (3) ◽  
pp. 5-15
Author(s):  
Muhammet Sinan Basarslan ◽  
Fatih Kayaalp
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
The Social ◽  
Vector Machines ◽  
Artificial Neural ◽  
Use Of The Internet ◽  
Python Programming

Social media has become an important part of our everyday life due to the widespread use of the Internet. Of the social media services, Twitter is among the most used ones around the world. People share their opinions by writing tweets about numerous subjects, such as politics, sports, economy, etc. Millions of tweets per day create a huge dataset, which drew attention of the data scientists to focus on these data for sentiment analysis. The sentiment analysis focuses to identify the social media posts of users about a specific topic and categorize them as positive, negative or neutral. Thus, the study aims to investigate the effect of types of text representation on the performance of sentiment analysis. In this study, two datasets were used in the experiments. The first one is the user reviews about movies from the IMDB, which has been labeled by Kotzias, and the second one is the Twitter tweets, including the tweets of users about health topic in English in 2019, collected using the Twitter API. The Python programming language was used in the study both for implementing the classification models using the Naïve Bayes (NB), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) algorithms, and for categorizing the sentiments as positive, negative and neutral. The feature extraction from the dataset was performed using Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec (W2V) modeling techniques. The success percentages of the classification algorithms were compared at the end. According to the experimental results, Artificial Neural Network had the best accuracy performance in both datasets compared to the others.

scholarly journals Analisis Sentimen Data Twitter Tentang Pasangan Capres-Cawapres Pemilu 2019 Dengan Metode Lexicon Based Dan Support Vector Machine

2019 ◽  
Vol 11 (2) ◽  
pp. 144
Author(s):  
Danar Wido Seno ◽  
Arief Wibowo
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Vice President ◽  
Training Data ◽  
Support Vector ◽  
New Words ◽  
Textual Data ◽  
Data Content ◽  
Combination Of Methods

Social media writing content growing make a lot of new words that appear on Twitter in the form of words and abbreviations that appear so that sentiment analysis is increasingly difficult to get high accuracy of textual data on Twitter social media. In this study, the authors conducted research on sentiment analysis of the pairs of candidates for President and Vice President of Indonesia in the 2019 Elections. To obtain higher accuracy results and accommodate the problem of textual data development on Twitter, the authors conducted a combination of methods to conduct the sentiment analysis with unsupervised and supervised methods. namely Lexicon Based. This study used Twitter data in October 2018 using the search keywords with the names of each pair of candidates for President and Vice President of the 2019 Elections totaling 800 datasets. From the study with 800 datasets the best accuracy was obtained with a value of 92.5% with 80% training data composition and 20% testing data with a Precision value in each class between 85.7% - 97.2% and Recall value for each class among 78, 2% - 93.5%. With the Lexicon Based method as a labeling dataset, the process of labeling the Support Vector Machine dataset is no longer done manually but is processed by the Lexicon Based method and the dictionary on the lexicon can be added along with the development of data content on Twitter social media.

scholarly journals KLASIFIKASI TEKS SOSIAL MEDIA TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE (Studi Kasus Penusukan Wiranto)

2019 ◽  
Vol 2 (2) ◽  
pp. 43
Author(s):  
Lalu Mutawalli ◽  
Mohammad Taufan Asri Zaen ◽  
Wire Bagye
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Big Data ◽  
Mass Communication ◽  
Confusion Matrix ◽  
Classification Model ◽  
Support Vector ◽  
Large Set ◽  
Appearance Time ◽  
Data Production

In the era of technological disruption of mass communication, social media became a reference in absorbing public opinion. The digitalization of data is very rapidly produced by social media users because it is an attempt to represent the feelings of the audience. Data production in question is the user posts the status and comments on social media. Data production by the public in social media raises a very large set of data or can be referred to as big data. Big data is a collection of data sets in very large numbers, complex, has a relatively fast appearance time, so that makes it difficult to handle. Analysis of big data with data mining methods to get knowledge patterns in it. This study analyzes the sentiments of netizens on Twitter social media on Mr. Wiranto stabbing case. The results of the sentiment analysis showed 41% gave positive comments, 29% commented neutrally, and 29% commented negatively on events. Besides, modeling of the data is carried out using a support vector machine algorithm to create a system capable of classifying positive, neutral, and negative connotations. The classification model that has been made is then tested using the confusion matrix technique with each result is a precision value of 83%, a recall value of 80%, and finally, as much as 80% obtained in testing the accuracy.

Sentiment Analysis with Social Media Analytics, Methods, Process, and Applications

2017 ◽  
pp. 192-208
Author(s):  
Karteek Ramalinga Ponnuru ◽  
Rashik Gupta ◽  
Shrawan Kumar Trivedi
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Social Media Analytics ◽  
Nearest Neighbour ◽  
Huge Amount ◽  
Word Clouds

Firms are turning their eye towards social media analytics to get to know what people are really talking about their firm or their product. With the huge amount of buzz being created online about anything and everything social media has become ‘the' platform of the day to understand what public on a whole are talking about a particular product and the process of converting all the talking into valuable information is called Sentiment Analysis. Sentiment Analysis is a process of identifying and categorizing a piece of text into positive or negative so as to understand the sentiment of the users. This chapter would take the reader through basic sentiment classifiers like building word clouds, commonality clouds, dendrograms and comparison clouds to advanced algorithms like K Nearest Neighbour, Naïve Biased Algorithm and Support Vector Machine.

Ooredoo Rayek

2020 ◽  
Vol 11 (2) ◽  
pp. 66-81
Author(s):  
Badia Klouche ◽  
Sidi Mohamed Benslimane ◽  
Sakina Rim Bennabi
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Experimental Results ◽  
Support Vector ◽  
Textual Data ◽  
New Strategy ◽  
Set Up

Sentiment analysis is one of the recent areas of emerging research in the classification of sentiment polarity and text mining, particularly with the considerable number of opinions available on social media. The Algerian Operator Telephone Ooredoo, as other operators, deploys in its new strategy to conquer new customers, by exploiting their opinions through a sentiments analysis. The purpose of this work is to set up a system called “Ooredoo Rayek”, whose objective is to collect, transliterate, translate and classify the textual data expressed by the Ooredoo operator's customers. This article developed a set of rules allowing the transliteration from Algerian Arabizi to Algerian dialect. Furthermore, the authors used Naïve Bayes (NB) and (Support Vector Machine) SVM classifiers to assign polarity tags to Facebook comments from the official pages of Ooredoo written in multilingual and multi-dialect context. Experimental results show that the system obtains good performance with 83% of accuracy.

scholarly journals PUBLIC’S SENTIMENT ANALYSIS ON SHOPEE-FOOD SERVICE USING LEXICON-BASED AND SUPPORT VECTOR MACHINE

2021 ◽  
Vol 4 (1) ◽  
pp. 1-8
Author(s):  
Shafira Shalehanny ◽  
Agung Triayudi ◽  
Endah Tri Esti Handayani
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Food Service ◽  
Support Vector ◽  
Accuracy Score ◽  
Online Business ◽  
Data Source ◽  
Sentiment Score ◽  
Processing Language

Technology field following how era keep evolving. Social media already on everyone’s daily life and being a place for writing their opinion, either review or response for product and service that already being used. Twitter are one of popular social media on Indonesia, according to Statista data it reach 17.55 million users. For online business sector, knowing sentiment score are really important to stepping up their business. The use of machine learning, NLP (Natural Processing Language), and text mining for knowing the real meaning of opinion words given by customer called sentiment analysis. Two methods are using for data testing, the first is Lexicon Based and the second is Support Vector Machine (SVM). Data source that used for sentiment analyst are from keyword ‘ShopeeFood’ and ‘syopifud’. The result of analysis giving accuracy score 87%, precision score 81%, recall score 75%, and f1-score 78%.

scholarly journals Studi Komparatif Metode Ekstraksi Fitur pada Analisis Sentimen Maskapai Penerbangan Menggunakan Support Vector Machine dan Maximum Entropy

2019 ◽  
Vol 3 (3) ◽  
pp. 402-407 ◽  
Author(s):  
Mona Cindo ◽  
Dian Palupi Rini ◽  
Ermatita
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Maximum Entropy ◽  
Entropy Method ◽  
Support Vector ◽  
Machine Method ◽  
N Gram ◽  
Almost All

Almost all companies use social media to improve their product services and provide after-sales services that allow their customers to review the quality of their products. By using Twitter social media to be an important source for tracking sentiment analysis. Sentiment analysis is one of the most popular studies today, using sentiment analysis companies can analyze customer satisfaction to improve their services. This study aims to analyze airline sentiments with five different features such as pragmatic, lexical n-gram, POS, sentiment, and LDA using the Support Vector Machine and Maximum Entropy methods. The best results can be obtained using the Maximum Entropy method using all feature extraction with an accuracy of 92.7% and in the Support Vector Machine method, the accuracy obtained is 89.2%.

scholarly journals SENTIMENT ANALYSIS ON TWITTER BY USING MAXIMUM ENTROPY AND SUPPORT VECTOR MACHINE METHOD

SINERGI ◽  
2020 ◽  
Vol 24 (2) ◽  
pp. 87
Author(s):  
Mona Cindo ◽  
Dian Palupi Rini ◽  
Ermatita Ermatita
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Product Quality ◽  
Sentiment Analysis ◽  
Maximum Entropy ◽  
The Other ◽  
Classification Method ◽  
Support Vector ◽  
General Opinion

With the advancement of social media and its growth, there is a lot of data that can be presented for research in social mining. Twitter is a microblogging that can be used. In this event, a lot of companies used the data on Twitter to analyze the satisfaction of their customer about product quality. On the other hand, a lot of users use social media to express their daily emotions. The case can be developed into a research study that can be used both to improve product quality, as well as to analyze the opinion on certain events. The research is often called sentiment analysis or opinion mining. While The previous research does a particularly useful feature for sentiment analysis, but it is still a lack of performance. Furthermore, they used Support Vector Machine as a classification method. On the other hand, most researchers found another classification method, which is considered more efficient such as Maximum Entropy. So, this research used two types of a dataset, the general opinion data, and the airline's opinion data. For feature extraction, we employ four feature extraction, such as pragmatic, lexical-grams, pos-grams, and sentiment lexical. For the classification, we use both of Support Vector Machine and Maximum Entropy to find the best result. In the end, the best result is performed by Maximum Entropy with 85,8% accuracy on general opinion data, and 92,6% accuracy on airlines opinion data.

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