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"This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on text embedded with heterogeneous and multimedia data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases."

"This book discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management."

"Era baru globalisasi mempengaruhi perkembangan teknologi, salah satunya yang menjadi indikator dalam majunya teknologi adalah kemajuan akan sumber data yang bisa diperoleh dengan mudah. Adalah Big Data yang mana suatu tempat di dalam internet yang berisi kumpulan data yang bervolume atau memiliki jumlah yang sangat banyak dan kompleks, alhasil tidak dapat untuk diproses menggunakan alat pengambil data secara konvensional melainkan membutuhkan perangkat bantu (software) dan perangkat bantu yang kinerjanya sesuai. Media sosial seperti yang diketahui oleh banyak orang adalah alat penting dalam berkomunikasi dimana dapat dilakukan secara online. Media sosial membuat masyarakat dapat berkomunikasi dengan mudah dan mempersingkat waktu. Penelitian menemukan bahwa penduduk Indonesia adalah termasuk pengguna jejaring sosial terbesar ke-4 didunia menurut data dari Hootsuit We Are Social tahun 2020. Twitter adalah media sosial terbesar ke 4 di Indonesia sebagai pengguna terbanyak. Data opini mengenai kebijakan pemerintah terkait wakaf tunai dan zakat profesi menjadi sumber yang penting untuk diolah sebagai data mining atau penambangan data. Tujuan dari penelitian ini adalah melihat sentimen dari masyarakat tekait wakaf tunai dan zakat profesi menggunakan Algoritma Naïve Bayes Classifier (NBC) menggunakan Analilis NVIVO 12 untuk analisa frekuensi kata. Penulis menggunakan labelling sentimen menggunakan metode Naive Bayes . Hasil akurasi data 77.5%, sebesar 52% sentiment Negatif dan 48% sentiment positif, wakaf tunai memiliki akurasi naïve bayes 70,3%, sentiment negatif 62% dan positif 37%.

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The new era of globalization affects the development of technology, the indicator in advancing technology is the progress of data sources that can be reached easily. The Big Data, which is a resource on the internet that contains a large volume of data collection. Social media, as many people known, is an important tool in communicating, which can be done online. Social media allows people to communicate easily in shortens time. The study found that the Indonesian population is the 4th largest social network user in the world, according to data from Hootsuite We Are Social in 2020. Twitter is the 4th largest social media user in Indonesia as the most users. On the other hand, cash waqf and zakah profession are the tools to eradicate poverty in Islamic Economics, it is also has large potential but poorly managed and has minimum collection of zakah and cash waqf. The Policy in making zakah on profession and cash waqf as the obligation to be paid for all moeslem civil servant in Indonesia should be identifies through research. The research of correlation between the more people trust in finance institution the more people will pay or put their money on the institution of bank or finance. The purpose of this study is to look at the sentiments of the public regarding cash waqf and professional zakat using the Naïve Bayes Classifier (NBC) Algorithm using NVIVO 12 Analys for word frequency analysis. The author uses sentiment labelling using the Naïve Bayes method. The results of data accuracy are 77.5%, 52% negative sentiment and 48% positive sentiment, cash waqf has nave Bayes accuracy of 70.3%, negative sentiment 62% and positive 37%. The result is people in social media twitter did not trust to the regulation of obligation in paying zakah profession and cash waqf. Distrusted issue is the main issue"

"ABSTRAKSkripsi ini bertujuan untuk mengetahui kepuasan pelanggan layanan data operator CDMA di Indonesia dengan menggunakan pesan tweet sebagai data kepuasan pelanggan real time. Data tersebut diolah menggunakan text mining dan sentiment analysis dengan membuat model klasifikasi teks. Tingkat akurasi model yang dibuat untuk memprediksi sentimen dari pesan tweet mencapai 80 %. Hasil penelitian menunjukkan bahwa pelanggan data operator CDMA di Indonesia baik secara umum maupun pada masing-masing operator cenderung tidak puas dengan layanan data yang diberikan. Secara umum kriteria kemudahan koneksi paling mempengaruhi ketidakpuasan pelanggan layanan data operator CDMA di Indonesia. Sedangkan kriteria kemudahan koneksi paling mempengaruhi ketidakpuasan pelanggan layanan data operator CDMA 1. Kemudian kriteria kemudahan koneksi dan kehandalan jaringan paling mempengaruhi ketidakpuasan pelanggan layanan data operator CDMA 2.

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ABSTRACTThis thesis aims to gain insight of customer satisfaction of Indonesian CDMA data services operators by using tweets as real time customer satisfaction data. The data is processed using text mining and sentiment analysis by creating text classification model. The model accuracy to predict sentiment of a tweet achieve 80%. The results showed that Indonesia CDMA data subcribers in general or to individual operators tend to not satisfied with the service provided. Connection easiness criteria most influencing customer dissatisfaction of Indonesia CDMA data service operators in general. While, the connection easiness criteria most influencing customer dissatisfaction of CDMA data service operator 1. Then, Connection easiness and network reliability criteria most influencing customer dissatisfaction of CDMA data service operator 2."

"Summary:Equips you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets. This title focuses on important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data."