ANALISIS SENTIMEN TERHADAP PROGRAM MERDEKA BELAJAR – KAMPUS MERDEKA PADA TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE

Merdeka Belajar - Kampus Merdeka Program as one of the public policies by the Ministry of Education, Culture, Research, and Technology, cannot be separated from public opinion. Opinions are divided into three categories, positive opinions, negative opinions, and neutral opinions. The public expresses opinions through various social media platforms. As a simple social media, Twitter actually has an influence on the process of forming and directing public opinion. Tweet data with the keyword Merdeka Belajar - Kampus Merdeka between August 2020 and February 2021, is used to conduct sentiment analysis to identify the direction of public opinion towards Merdeka Belajar - Kampus Merdeka Program. Sentiment analysis using Support Vector Machine can be applied to predict the direction of a person's sentiment towards the Program Merdeka Belajar – Kampus Merdeka, both positive and negative sentiments. Based on the test results, it can be concluded that the F-measure accuracy value for the positive class is 94.8% and the F-measure accuracy value for the negative class is 95%.Program Merdeka Belajar - Kampus Merdeka sebagai salah satu kebijakan publik oleh Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi, tidak lepas dari opini publik. Arah opini terbagi dalam tiga kategori, opini positif, opini negatif, dan opini netral. Publik menyampaikan opini melalui berbagai platform media sosial. Sebagai salah satu media sosial yang sederhana, Twitter justru memiliki pengaruh terhadap proses pembentukan dan pengarahan opini publik. Memanfaatkan data tweet dengan keyword Merdeka Belajar - Kampus Merdeka antara Agustus 2020 sampai dengan Febuari 2021, analisis sentimen dilakukan untuk mengenali opini publik terhadap Program Merdeka Belajar - Kampus Merdeka. Analisis sentimen menggunakan Support Vector Machine dapat diterapkan untuk memprediksi arah sentimen seseorang terhadap Program Merdeka Belajar – Kampus Merdeka, baik sentimen positif maupun sentimen negatif. Berdasarkan hasil pengujian, dapat disimpulkan bahwa nilai akurasi F-measure untuk kelas positif sebesar 94.8% dan nilai akurasi F-measure untuk kelas negatif sebesar 95%

Multilingual Cross-domain Perspectives on Online Hate Speech

In this report, we present a study of eight corpora of online hate speech, by demonstrating the NLP techniques that we used to collect and analyze the jihadist, extremist, racist, and sexist content. Analysis of the multilingual corpora shows that the different contexts share certain characteristics in their hateful rhetoric. To expose the main features, we have focused on text classification, text profiling, keyword and collocation extraction, along with manual annotation and qualitative study.Comment: 24 page

Search engine For Twitter sentiment analysis

textThe purpose of sentiment analysis is to determine the attitude of a writer or a speaker with respect to some topic or his feeling in a document. Thanks to the rise of social media, nowadays there are numerous data generated by users. Mining and categorizing these data will not only bring profits for companies, but also benefit the nation. Sentiment analysis not only enables business decision makers to better understand customers' behaviors, but also allows customers to know how the public feel about a product before purchasing. On the other hand, the aggregation of emotions will effectively measure the public response toward an event or news. For example, the level of distress and sadness will increase significantly after terror attacks or natural disaster. In our project, we are going to build a search engine that allows users to check the sentiment of his query. Some of previous researches on classifying sentiment of messages on micro-blogging services like Twitter have tried to solve this problem but they have ignored neutral tweets, which will result in problematic results (12). Our sentiment analysis will also be based on tweets collected from twitter, since twitter can offer sufficient and real-time corpora for analysis. We will preprocess each tweet in the training set and label it as positive, negative or neutral. As we use words in the tweet as the feature for our model, different features will be used. We will show that accuracy achieved by different machine learning algorithms (Naïve Bayes, Maximum Entropy) can be improved with a feature vector obtained by using bigrams (5). In our practice, we find that Naive Bayes has better performance than Maximum Entropy.Statistic

Simple Text Mining for Sentiment Analysis of Political Figure Using Naive Bayes Classifier Method

Text mining can be applied to many fields. One of the application is using text mining in digital newspaper to do politic sentiment analysis. In this paper sentiment analysis is applied to get information from digital news articles about its positive or negative sentiment regarding particular politician. This paper suggests a simple model to analyze digital newspaper sentiment polarity using naive Bayes classifier method. The model uses a set of initial data to begin with which will be updated when new information appears. The model showed promising result when tested and can be implemented to some other sentiment analysis problems.Comment: 5 pages, published in the Proceedings of the 7th ICT