Augmenting Chinese Online Video Recommendations by Using Virtual Ratings Predicted by Review Sentiment Classification

Abstract—In this paper we aim to resolve the recommendation problem by using the virtual ratings in online environments when user rating information is not available. As a matter of fact, in most of current websites especially the Chinese video-sharing ones, the traditional pure rating based collaborative filtering recommender methods are not fully qualified due to the sparsity of rating data. Motivated by our prior work on the investigation of user reviews that broadly appear in such sites, we hence propose a new recommender algorithm by fusing a self-supervised emoticon-integrated sentiment classification approach, by which the missing User-Item Rating Matrix can be substituted by the virtual ratings which are predicted by decomposing user reviews as given to the items. To test the algorithm’s practical value, we have first identified the self-supervised sentiment classification’s higher performance by comparing it with a supervised approach. Moreover, we conducted a statistic evaluation method to show the effectiveness of our recommender system on improving Chinese online video recommendations ’ accuracy. Keywords-Information retrieval; sentiment analysis; opinion mining; online video recommendation. I

Analisis sentimen pada twitter mengenai program imunisasi measles rubella di Indonesia

Latar belakang:  Measles dan Rubella adalah dua penyakit yang sangat menular yang sampai saat ini belum ada obatnya. Imunisasi Measles Rubella (MR) adalah satu-satunya cara mencegah penyakit ini. Sehubungan awal diadakannya pemberian imunisasi MR di Indonesia tahun 2017 menyebabkan banyak orang menyampaikan opininya tentang imunisasi MR di berbagai media sosial, salah satunya melalui twitter. Analisis sentimen twitter dapat digunakan untuk menarik sebuah kesimpulan apakah pandangan masyarakat terhadap program imunisasi MR cenderung positif atau negatif. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat pada twitter mengenai program imunisasi MR di Indonesia.Metode Penelitian: Jenis penelitian kuantitatif dengan rancangan penelitian deskriptif. Subjek penelitian tweets yang menggunakan kata kunci “vaksin”, “imunisasi”, “vaksin mr”, “imunisasi mr”, dan tweets yang menggunakan #vaksinmr, #imunisasimr. Hanya tweets dalam Bahasa Indonesia diambil. Data Januari sampai September 2017 dikumpulkan, dipreprocessing, diklasifikasikan berdasarkan tahap pengetahuan persuasi dan tahap pengambilan keputusan menjadi sentimen positif, netral, dan negatif. Analisis menggunakan tool Rapidminer versi 8.1 dengan metode klasifikasi K-NN.Hasil: Pada tahap pengetahuan dan persuasi, sentimen netral lebih banyak muncul dengan pelabelan manual dan sentimen positif lebih banyak muncul dengan RapidMiner. Pada tahap pengambilan keputusan, sentimen netral lebih banyak muncul dengan pelabelan manual dan RapidMiner.Kesimpulan: Twitter dapat digunakan memprediksi opini masyarakat tentang imunisasi MR di Indonesia. Namun untuk prediksi manual diperlukan waktu banyak sehingga diperlukan tool yang dapat membantu mempercepat prediksi tetapi tool tersebut perlu memperhatikan keseimbangan data latih dan penggunaan dataset yang di dalamnya terdapat kata yang sama. Kata kunci: Analisis Sentimen, Imunisasi, Measles Rubella, Twitte

Sentiment Analysis of User Comments for One-Class Collaborative Filtering over TED Talks

User-generated texts such as reviews, comments or discussions are valuable indicators of users’ preferences. Unlike previous works which focus on labeled data from user-contributed reviews, we focus here on user comments which are not accompanied by pre-defined rating labels. We investigate their role in a one-class collaborative filtering task such as bookmarking, where only the user action is given as ground-truth. We propose a sentiment-aware nearest neighbor model (SANN) for multimedia recommendations over TED talks, which makes use of user comments. The model outperforms significantly (by more than 25% on unseen data) several competitive baselines

Text-based user-kNN:measuring user similarity based on text reviews

This article reports on a modification of the user-kNN algorithm that measures the similarity between users based on the similarity of text reviews, instead of ratings. We investigate the performance of text semantic similarity measures and we evaluate our text-based user-kNN approach by comparing it to a range of ratings-based approaches in a ratings prediction task. We do so by using datasets from two different domains: movies from RottenTomatoes and Audio CDs from Amazon Products. Our results show that the text-based userkNN algorithm performs significantly better than the ratings-based approaches in terms of accuracy measured using RMSE