APLIKASI SENTIMENT MONITORING UNTUK TWITTER DENGAN ALGORITMA NAIVE-BAYES CLASSIFIER

Every day there are millions of opinion spread across social networks. This is often utilized by various parties to determine the opinion and sentiment of the public towards the product, brand or figures that they hold. Given the abundance of data and opinions, it is not possible to do sentiment analysis manually. In this research, author performs design and implementation of sentiment monitoring application, that could monitor people’s sentiment about a particular keyword, so it is known how the people response to those keywords, whether positive, negative or neutral. From various existing social networks, Twitter is chosen as the source of data that will be monitored. Classification algorithm used here is Naive-Bayes Classifier with Boolean Multinomial model, and feature extraction using unigram word. The training data used is 400,000 data for each type of sentiment, so the total is 1.200.000 data. In the process of classification and training, application will  perform  stemming  to  take  the  root  words  contained  within  the  tweet. Stemming algorithm used here is Confix Stripping. The  methodology  of  application  development  that  used  here is  staged delivery. Implementation of application is done using PHP programming language. The result of this research is a sentiment monitoring application that can monitor public sentiment about a particular keyword in a particular time frame. From testing using k-fold cross validation, obtained accuracy rate for sentiment classification amounted to 85%

Analisis Sentimen Twitter Debat Calon Presiden Indonesia Menggunakan Metode Fined-Grained Sentiment Analysis

Media sosial, Twitter, saat ini telah banyak memberikan dampak besar dalam membangun opini, pandangan, sentimen, dan preferensi politik publik (menjelang Pemilihan Umum) berlangsung. Penelitian ini dilakukan untuk mengetahui percakapan di Twitter pada debat pertama calon presiden Republik Indonesia melalui hashtag dari kedua pasang calon. Selain itu, juga untuk mengetahui tentang kecenderungan masyarakat di Twitter terkait dengan debat yang sedang berlangsung tersebut cenderung positif, negatif, atau netral. Data percakapan di Twitter didapatkan melalui Twitter API yang diambil dengan bahasa Pemrograman R. Proses analisis sentimen ini menggunakan metode Fined-grained Sentiment Analysis yaitu, Jika satu tweet berisi lebih banyak kalimat positif daripada negatif, maka hasil keseluruhan akan positif dan bernilai (+1). Jika jumlah kalimat negatif lebih besar dari kalimat positif, maka hasil keseluruhan negatif dan bernilai (-1). Jika ada jumlah yang sama dari kalimat positif dan negatif dalam paragraf, maka hasilnya adalah netral dan bernilai (0). Hasil dari penelitian ini menunjukkan bahwa tweet sentimen dari kedua hashtag cenderung positif, lebih banyak daripada sentimen negatif dan netral

A Model for Predicting Music Popularity on Streaming Platforms

The global music market moves billions of dollars every year, most of which comes from streamingplatforms. In this paper, we present a model for predicting whether or not a song will appear in Spotify’s Top 50, a ranking of the 50 most popular songs in Spotify, which is one of today’s biggest streaming services. To make this prediction, we trained different classifiers with information from audio features from songs that appeared in this ranking between November 2018 and January 2019. When tested with data from June and July 2019, an SVM classifier with RBF kernel obtained accuracy, precision, and AUC above 80%

Accurately predicting hit songs using neurophysiology and machine learning

Identifying hit songs is notoriously difficult. Traditionally, song elements have been measured from large databases to identify the lyrical aspects of hits. We took a different methodological approach, measuring neurophysiologic responses to a set of songs provided by a streaming music service that identified hits and flops. We compared several statistical approaches to examine the predictive accuracy of each technique. A linear statistical model using two neural measures identified hits with 69% accuracy. Then, we created a synthetic set data and applied ensemble machine learning to capture inherent non-linearities in neural data. This model classified hit songs with 97% accuracy. Applying machine learning to the neural response to 1st min of songs accurately classified hits 82% of the time showing that the brain rapidly identifies hit music. Our results demonstrate that applying machine learning to neural data can substantially increase classification accuracy for difficult to predict market outcomes

Temporal Success Analyses in Music Collaboration Networks: Brazilian and Global Scenarios

Collaboration is a part of the music industry and has increased over recent decades; but little do we know about its effects on success and evolution. Our goal is to analyze how success has evolved over collaboration networks and compare its global scenario to a local, thriving one: the Brazilian music industry. Specifically, we build collaboration networks from data collected from Spotify's Global and Brazilian daily charts, analyze them and identify collaboration profiles in such networks. Analyses over their topological characteristics reveal collaboration patterns mapped into four different profiles: Standard, Niche, Ephemeral and Absent, where the two first have a higher level of success. Furthermore, we do deeper by evaluating the temporal evolution of such profiles through case studies: pop and k-pop globally, and pop and forró in Brazil. Overall, our findings emphasize the importance of collaboration profiles in assessing success, and show differences between the global and Brazilian scenarios

Temporal Success Analyses in Music Collaboration Networks: Brazilian and Global Scenarios

Collaboration is a part of the music industry and has increased over recent decades; but little do we know about its effects on success and evolution. Our goal is to analyze how success has evolved over collaboration networks and compare its global scenario to a local, thriving one: the Brazilian music industry. Specifically, we build collaboration networks from data collected from Spotify's Global and Brazilian daily charts, analyze them and identify collaboration profiles in such networks. Analyses over their topological characteristics reveal collaboration patterns mapped into four different profiles: Standard, Niche, Ephemeral and Absent, where the two first have a higher level of success. Furthermore, we do deeper by evaluating the temporal evolution of such profiles through case studies: pop and k-pop globally, and pop and forró in Brazil. Overall, our findings emphasize the importance of collaboration profiles in assessing success, and show differences between the global and Brazilian scenarios