Implementasi Recommender System Based on Sentiment Classification Melalui Opinion Extraction (Studi Kasus Review User di Google Play)

ABSTRAKSI: Sentiment Classification dapat memberikan nilai pada suatu teks apakah teks tersebut termasuk ke dalam “negatif”, “netral” atau “positif”. Dari Sentiment Classification tersebut dapat memberikan suatu rekomendasi kepada pengguna dalam bentuk tekstual. Recommender System dapat dijadikan cara untuk memberikan rekomendasi suatu produk baru kepada pengguna. Sebagian besar deskripsi aplikasi/produk, opini dari pengguna dan sebagainya disajikan dalam bentuk tekstual di website. Ada banyak cara dalam menilai suatu produk yang ditawarkan, contohnya ialah dengan pengguna memberi penilaian dengan memberi “Like” atau “Dislike” atau pengguna memberikan bintang dari skala 1(kurang bagus) sampai 5 (sangat bagus. Pada Tugas akhir ini, Sentiment Classification akan dilakukan melalui Opinion Extraction, dimana dalam tahap ini akan dilakukan pengolahan kata fitur produk dan kata opini, proses parsing dengan menggunakan Stanford Parser untuk mendapatkan hubungan gramatikal dalam setiap kalimat, menentukan pasangan kata fitur produk dan opini, menentukan kekuatan dan polaritas kata opini melalui SentiWordNet lalu mengakumulasi nilai akhir untuk setiap review. Jika nilai akhir Sentiment tersebut positif maka pengguna tersebut merekomendasikan aplikasi yang dibicarakan, jika negatif sebaliknya. Kemudian dengan menggunakan teknik Item Based Collaborative Filtering Recommender System, kita dapat memberikan suatu rekomendasi kepada pengguna berdasarkan dari aplikasi-aplikasi yang pernah mereka nilai sebelumnya. Berdasarkan hasil pengujian menggunakan metode Mean Opinion Score (MOS), Recommender System yang dibangun dengan Sentiment Classification melalui Opinion Extraction dapat menjamin hasil nilai total sentimen dengan akurasi sebesar 92% dan akurasi untuk hasil aplikasi rekomendasi sebesar 83%.Kata Kunci : Opinion Extraction, Sentiment Classification , Recommender System, Collaborative FilteringABSTRACT: Sentiment Classification can provide value in a text is the text belong to the "negative", "positive" or "neutral". The Classification of Sentiment can provide a recommendation to the user in the form of a textual. Recommender System can be used as a way to give a new product recommendations to users. Most of the application/product descriptions, opinions from users and so on are presented in textual form on the website. There are many ways in assessing a product offered, for example, is to give users an assessment by giving it a "Like" or "Dislike" or the user gives the stars from a scale of 1 (not good) to 5 (very good). In this final Task, Sentiment Classification will be done through Opinion Extraction, where in this phase will be done word processing product features and opinions, said the process of parsing using the Stanford Parser to get the grammatical relationship in every phrase, word pairs to determine product features and opinions, determines the strength and polarity of opinion through SentiWordNet words then accumulate final value to each review. If the value of the end of the positive Sentiment that user then recommend applications that talk about, if negative otherwise. Then by using the techniques of Item Based Collaborative Filtering Recommender System, we can provide a recommendation to the user based on the applications they\u27ve ever scored before. Based on the results of testing method using Mean Opinion Score (MOS), the Recommender System based on Sentiment Classification through Opinion Extraction can guarantee the result of a total value of sentiment with the accuracy of 92% for accuracy and results of application recommended by 83%.Keyword: Opinion Extraction, Sentiment Classification, Recommender System, Collaborative Filterin

Modeling User Preferences in Recommender Systems: A Classification Framework for Explicit and Implicit User Feedback

Recommender systems are firmly established as a standard technology for assisting users with their choices; however, little attention has been paid to the application of the user model in recommender systems, particularly the variability and noise that are an intrinsic part of human behavior and activity. To enable recommender systems to suggest items that are useful to a particular user, it can be essential to understand the user and his or her interactions with the system. These interactions typically manifest themselves as explicit and implicit user feedback that provides the key indicators for modeling users' preferences for items and essential information for personalizing recommendations. In this article, we propose a classification framework for the use of explicit and implicit user feedback in recommender systems based on a set of distinct properties that include Cognitive Effort, UserModel, Scale of Measurement, and Domain Relevance.We develop a set of comparison criteria for explicit and implicit user feedback to emphasize the key properties. Using our framework, we provide a classification of recommender systems that have addressed questions about user feedback, and we review state-of-the-art techniques to improve such user feedback and thereby improve the performance of the recommender system. Finally, we formulate challenges for future research on improvement of user feedback. © 2014 ACM

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Toward a collective intelligence recommender system for education

The development of Information and Communication Technology (ICT), have revolutionized the world and have moved us into the information age, however the access and handling of this large amount of information is causing valuable time losses. Teachers in Higher Education especially use the Internet as a tool to consult materials and content for the development of the subjects. The internet has very broad services, and sometimes it is difficult for users to find the contents in an easy and fast way. This problem is increasing at the time, causing that students spend a lot of time in search information rather than in synthesis, analysis and construction of new knowledge. In this context, several questions have emerged: Is it possible to design learning activities that allow us to value the information search and to encourage collective participation?. What are the conditions that an ICT tool that supports a process of information search has to have to optimize the student's time and learning? This article presents the use and application of a Recommender System (RS) designed on paradigms of Collective Intelligence (CI). The RS designed encourages the collective learning and the authentic participation of the students. The research combines the literature study with the analysis of the ICT tools that have emerged in the field of the CI and RS. Also, Design-Based Research (DBR) was used to compile and summarize collective intelligence approaches and filtering techniques reported in the literature in Higher Education as well as to incrementally improving the tool. Several are the benefits that have been evidenced as a result of the exploratory study carried out. Among them the following stand out: • It improves student motivation, as it helps you discover new content of interest in an easy way. • It saves time in the search and classification of teaching material of interest. • It fosters specialized reading, inspires competence as a means of learning. • It gives the teacher the ability to generate reports of trends and behaviors of their students, real-time assessment of the quality of learning material. The authors consider that the use of ICT tools that combine the paradigms of the CI and RS presented in this work, are a tool that improves the construction of student knowledge and motivates their collective development in cyberspace, in addition, the model of Filltering Contents used supports the design of models and strategies of collective intelligence in Higher Education.Postprint (author's final draft

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0

Recommending with an Agenda: Active Learning of Private Attributes using Matrix Factorization

Recommender systems leverage user demographic information, such as age, gender, etc., to personalize recommendations and better place their targeted ads. Oftentimes, users do not volunteer this information due to privacy concerns, or due to a lack of initiative in filling out their online profiles. We illustrate a new threat in which a recommender learns private attributes of users who do not voluntarily disclose them. We design both passive and active attacks that solicit ratings for strategically selected items, and could thus be used by a recommender system to pursue this hidden agenda. Our methods are based on a novel usage of Bayesian matrix factorization in an active learning setting. Evaluations on multiple datasets illustrate that such attacks are indeed feasible and use significantly fewer rated items than static inference methods. Importantly, they succeed without sacrificing the quality of recommendations to users.Comment: This is the extended version of a paper that appeared in ACM RecSys 201