Opinion Summarization Fitur Produk Elektronik Pada Amazon.com Dengan Metode Maximum Entropy

ABSTRAKSI: Jumlah pelanggan toko online meningkat pesat seiring menjamurnya e-commerce dan meningkatnya jumlah para pedagang online. Para pelanggan dapat mereview produk secara online. Review pelanggan ini menjadi suatu sumber informasi yang sangat berguna baik bagi pelanggan maupun produk manufaktur. Pelanggan dapat menggunakan informasi tersebut untuk mendukung keputusan mereka dalam membeli suatu barang. Bagi produk manufaktur, mengerti pendapat pelanggan merupakan informasi yang berharga untuk perkembangan suatu produk, pemasaran, dan juga CRM (Customer Relationship Management). Tetapi dengan semakin banyaknya review suatu produk, memunculkan masalah yaitu menyulitkan pelangggan maupun produk manufaktur dalam mengevaluasi review yang ada.Jumlah pelanggan toko online meningkat pesat seiring menjamurnya e-commerce dan meningkatnya jumlah para pedagang online. Para pelanggan dapat mereview produk secara online. Review pelanggan ini menjadi suatu sumber informasi yang sangat berguna baik bagi pelanggan maupun produk manufaktur. Pelanggan dapat menggunakan informasi tersebut untuk mendukung keputusan mereka dalam membeli suatu barang. Bagi produk manufaktur, mengerti pendapat pelanggan merupakan informasi yang berharga untuk perkembangan suatu produk, pemasaran, dan juga CRM (Customer Relationship Management). Tetapi dengan semakin banyaknya review suatu produk, memunculkan masalah yaitu menyulitkan pelangggan maupun produk manufaktur dalam mengevaluasi review yang ada.Berdasarkan hasil pengujian didapatkan bahwa menggunakan metode klasifikasi maximum entropy menghasilkan performansi yang lebih baik daripada tanpa menggunakan maximume entropy.Kata Kunci : Data Mining, Opinion Mining, Opinion Summarization, Pos Tagging, Maximum EntropyABSTRACT: The number of customers increases significantly as the online shop e-commerce proliferation and the increasing number of online merchants. The costumers can review products online. Review from costumers is a source of information that is very useful for both the consumer and manufacturing products. The costumers can use the information to support their decision in purchasing an item. For manufactured products, understanding the customer\u27s opinion is valuable information for the development of a product, marketing, and also CRM (Customer Relationship Management). But with the increasing number of reviews of a product, raise issues which complicate the costumers and manufacturing products in evaluating the existing review.This thesis aims to summarize the existing reviews by grouping based on features and orientation of the opinion. Each review will be looked for the features that are discussed and defined the orientation of the opinion. There are three stages: (1) extracting the features of a product and identifying opinion related to the featured products in each sentence (feature extraction); (2) Identifying orientation of the opinion (sentiment analysis); (3) Generating summarize based on feature and orientation.Maximum entropy classification method used to classify the extracted features. Based on the test result found that using maximum entropy classification methods produce better performance than without using maximum entropyKeyword: Data Mining, Opinion Mining, Opinion Summarization, Pos Tagging, Maximum Entrop

Research Directions, Challenges and Issues in Opinion Mining

Rapid growth of Internet and availability of user reviews on the web for any product has provided a need for an effective system to analyze the web reviews. Such reviews are useful to some extent, promising both the customers and product manufacturers. For any popular product, the number of reviews can be in hundreds or even thousands. This creates difficulty for a customer to analyze them and make important decisions on whether to purchase the product or to not. Mining such product reviews or opinions is termed as opinion mining which is broadly classified into two main categories namely facts and opinions. Though there are several approaches for opinion mining, there remains a challenge to decide on the recommendation provided by the system. In this paper, we analyze the basics of opinion mining, challenges, pros & cons of past opinion mining systems and provide some directions for the future research work, focusing on the challenges and issues

Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

Optical tomography: Image improvement using mixed projection of parallel and fan beam modes

Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be defined by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The findings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam