Komparasi Metode K-Nearest Neighbors dan Support Vector Machine Pada Sentiment Analysis Review Kamera

Abstract - Sentiment analysis is becoming one of the research growing trend, especially in text classification. In this study, the authors use as a camera review dataset by comparing two methods of KNN and SVM. Each method trials conducted so as to produce Accuracy KNN = 79.00% and the AUC of 0.929. While the data processing method SVM its accuracy is 72.00% and the AUC of 0845. Based on these results, proving that the rate of KNN text classification using more accurate than the method of SVMKeywords: Sentiment Analysis, Review, KNN, SVM, Text Classification Abstrak – Sentiment Analisis menjadi salah satu trend riset yang semakin berkembang, khususnya dalam klasifikasi teks. Pada penelitian ini, penulis menggunakan review kamera sebagai dataset dengan membandingkan dua metode yaitu KNN dan SVM. Masing-masing metode dilakukan uji coba sehingga menghasilkan Akurasi KNN= 79.00% dan AUC sebesar 0.929. Sedangkan hasil pengolahan data metode SVM akurasi-nya adalah 72.00% dan AUC sebesar 0.845. Berdasarkan hasil penelitian tersebut, membuktikan bahwa tingkat klasifikasi teks menggunakan KNN lebih akurat dibandingkan dengan metode SVM.Kata Kunci: Analisis Sentimen, Ulasan, KNN, SVM, Klasifikasi Tek

Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions

In this paper, we examine the performance of several classifiers in the process of searching for very negative opinions. More precisely, we do an empirical study that analyzes the influence of three types of linguistic features (n-grams, word embeddings, and polarity lexicons) and their combinations when they are used to feed different supervised machine learning classifiers: Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM). The experiments we have carried out show that SVM clearly outperforms NB and DT in all datasets by taking into account all features individually as well as their combinationsThis research was funded by project TelePares (MINECO, ref:FFI2014-51978-C2-1-R), and the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016-2019, ED431G/08) and the European Regional Development Fund (ERDF)S

PENINGKATAN OPTIMASI SENTIMEN DALAM PELAKSANAAN PROSES PEMILIHAN PRESIDEN BERDASARKAN OPINI PUBLIK DENGAN MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN PARICLE SWARM OPTIMIZATION

Abstract- The development of increasingly advanced IT in the process of presidential elections. When the Presidential election of 2014 yesterday has a lot of people use the phrase does not educate inappropriate to be delivered among the public. Pros and cons indeed occur among people are so warm that they pour on the internet. This happens because when getting warm diperbincangan 2014 presidential election yesterday happened pengkubu-kubuan two candidates. Society can not adjust the development of IT process well. Naive Bayes is widely used for classification problems in data mining and machine learning for its simplicity and accuracy of classification impressive. Naive Bayes classifier has been shown to be very effective to solve the problem of large scale for text categorization with high accuracy. In addition to having many capabilities mentioned above, however this method has a drawback in the assumptions that are difficult to fulfill, namely the independence of the feature. Particle Swarm Optimization (PSO) is an evolutionary computation technique which is able to produce globally optimal solution in the search space through the interaction of individuals in a swarm of particles. PSO is widely used to solve optimization problems as well as the feature selection. Accuracy is generated on Naive Bayes algorithm amounted to 63.85% and AUC by 0523, while Naive Bayes and Particle Swarm Optimmization with an accuracy of 71.15% and the AUC of 0.600. It can be concluded that the application of optimization can improve the accuracy of 63.85% to 71.15%. Naive Bayes Model and Particle Swarm Optimization can provide solutions to the problems of classification review of public opinion news of the election in order to more accurately and optimally. Keywords:Public Opinion, Classification, Naive Bayes, Particle Swarm Optimization, Text Mining

Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach

In recent years, some methods of sentiment analysis have been developed for the health domain; however, the diabetes domain has not been explored yet. In addition, there is a lack of approaches that analyze the positive or negative orientation of each aspect contained in a document (a review, a piece of news, and a tweet, among others). Based on this understanding, we propose an aspect-level sentiment analysis method based on ontologies in the diabetes domain. The sentiment of the aspects is calculated by considering the words around the aspect which are obtained through N-gram methods (N-gram after, N-gram before, and N-gram around). To evaluate the effectiveness of our method, we obtained a corpus from Twitter, which has been manually labelled at aspect level as positive, negative, or neutral. The experimental results show that the best result was obtained through the N-gram around method with a precision of 81.93%, a recall of 81.13%, and an F-measure of 81.24%

Can social media predict soccer clubs' stock prices?: The case of Turkish teams

Finance literature in sports focuses on three main methods of stock price prediction in soccer: based on match results, pre-match expectations or match importance. For pre-match expectations, betting odds is commonly used as the indicator of investors' sentiments. We propose to include Twitter data as another indicator of this variable, and analyze the links between soccer match results, sentiments, and stock returns of the four major Turkish soccer teams. Our results show that social media can be a strong indicator of pre-match expectations and investors’ sentiments in stock price prediction