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

Review on recent advances in information mining from big consumer opinion data for product design

In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design

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

Analisis Sentimen Berdasarkan Fitur Produk Menggunakan Opinion Lexicon dan Wordnet

ABSTRAKSI: Transaksi online saat ini sudah menjadi bagian hidup dan digemari oleh banyak orang di dunia. Situs seperti Ebay.com dan Amazon.com misalnya, merupakan situs jual beli online yang sangat terkenal. Produk-produk yang dijual pun sangatlah beragam. Hal itu mengundang banyak orang untuk memberikan review terhadap produk-produk tersebut. Informasi berupa review yang diberikan tersebut akan sangat berguna baik untuk perusahaan pemilik produk maupun para calon pembeli atau pengunjung dari website tersebut. Akan tetapi dengan terus bertambahnya review mengenai suatu produk juga dapat membuat para kustomer menjadi kebingungan untuk menentukan produk mana yang sesuai dengan keinginannya. Oleh karena itulah perlu sekali adanya identifikasi kalimat opini yang berasal dari banyaknya review yang diberikan. Dengan begitu pembeli dapat mengetahui kelebihan dan kekurangan dari suatu produk yang ditawarkan.Tugas akhir ini dilakukan dengan melalui serangkaian tahapan yang terdiri dari tiga langkah utama yaitu : 1) melakukan identifikasi dan mengekstrak fitur produk dari review kustomer (feature extraction); 2) mengidentifikasi kalimat opini yang mengandung fitur produk untuk kemudian ditentukan orientasinya (sentiment analysis); 3) menghasilkan ringkasan yaitu berupa klasifikasi opini berdasarkan fitur produk (summary generation). Dataset yang digunakan berasal dari review kustomer di situs Amazon.com karena detail informasinya yang lengkap. Hasil akhir dari Tugas Akhir ini berupa sebuah ringkasan dalam bentuk file teks yang terdiri dari kalimat opini yang sudah dikelompokkan berdasarkan fitur produk dan orientasinya.Kata Kunci : Product Feature, Opinion Mining, Sentiment Analysis, Review Summarization.ABSTRACT: Online transactions are now a part of life and loved by many people in the world. Sites like Ebay.com and Amazon.com, for example, an online trading sites that are well known. The products sold were extremely diverse. It attracted many people to give a review of these products. Review of information provided will be very useful both for the company owners and potential buyers of products or visitors of the website. But with the continued increase in the reviews about a product can also create confusion for the customer to determine which products are in accordance with her wishes. Therefore it is essential to the identification of sentence opinion from the many reviews that are given. That way the buyer can find out the advantages and disadvantages of the products offered.The final task is done through a series of stages which consist of three main steps: 1) to identify and extract product features from customer review (feature extraction); 2) identifying opinion sentences that contain the product\u27s features and then determined the orientation (sentiment analysis); 3) produce a summary of which is a classification opinion based on the product\u27s features (summary generation). The dataset is derived from the customer reviews on Amazon.com website for complete detail information. The end result of this final project summary in the form of a text file that consists of opinion sentences that have been grouped based on the product\u27s features and orientation.Keyword: Product Feature, Opinion Mining, Sentiment Analysis, Review Summarization