다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Sentiment analysis is one of the major tasks of natural language processing, in which attitudes, thoughts, opinions, or judgments toward a particular subject has been extracted. Web is an unstructured and rich source of information containing many text documents with opinions and reviews. The recognition of sentiment can be helpful for individual decision makers, business organizations, and governments. In this article, we present a deep learning‐based approach to sentiment analysis on product reviews obtained from Twitter. The presented architecture combines TF‐IDF weighted Glove word embedding with CNN‐LSTM architecture. The CNN‐LSTM architecture consists of five layers, that is, weighted embedding layer, convolution layer (where, 1‐g, 2‐g, and 3‐g convolutions have been employed), max‐pooling layer, followed by LSTM, and dense layer. In the empirical analysis, the predictive performance of different word embedding schemes (ie, word2vec, fastText, GloVe, LDA2vec, and DOC2vec) with several weighting functions (ie, inverse document frequency, TF‐IDF, and smoothed inverse document frequency function) have been evaluated in conjunction with conventional deep neural network architectures. The empirical results indicate that the proposed deep learning architecture outperforms the conventional deep learning methods.

Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks

The paper surveys evaluation methods of natural language generation (NLG) systems that have been developed in the last few years We group NLG evaluation methods into three categories: (1) human-centric evaluation metrics, (2) automatic metrics that require no training, and (3) machine-learned metrics For each category, we discuss the progress that has been made and the challenges still being faced, with a focus on the evaluation of recently proposed NLG tasks and neural NLG models We then present two examples for task-specific NLG evaluations for automatic text summarization and long text generation, and conclude the paper by proposing future research directions

Evaluation of Text Generation: A Survey

In recent years, deep learning’s revolutionary advances in speech recognition, image analysis, and natural language processing have gained significant attention. Deep learning technology has become a hotspot research field in the artificial intelligence and has been applied into recommender system. In contrast to traditional recommendation models, deep learning is able to effectively capture the non-linear and non-trivial user-item relationships and enables the codification of more complex abstractions as data representations in the higher layers. In this paper, we provide a comprehensive review of the related research contents of deep learning-based recommender systems. First, we introduce the basic terminologies and the background concepts of recommender systems and deep learning technology. Second, we describe the main current research on deep learning-based recommender systems. Third, we provide the possible research directions of deep learning-based recommender systems in the future. Finally, concludes this paper.

A Survey of Recommender Systems Based on Deep Learning

Recommender systems have become a popular and effective means to manage the ever-increasing amount of multimedia content available today and to help users discover interesting new items. Today’s recommender systems suggest items of various media types, including audio, text, visual (images), and videos. In fact, scientific research related to the analysis of multimedia content has made possible effective content-based recommender systems capable of suggesting items based on an analysis of the features extracted from the item itself. The aim of this survey is to present a thorough review of the state-of-the-art of recommender systems that leverage multimedia content, by classifying the reviewed papers with respect to their media type, the techniques employed to extract and represent their content features, and the recommendation algorithm. Moreover, for each media type, we discuss various domains in which multimedia content plays a key role in human decision-making and is therefore considered in the recommendation process. Examples of the identified domains include fashion, tourism, food, media streaming, and e-commerce.

Recommender Systems Leveraging Multimedia Content

Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. This systematic literature review presents the state of the art in hybrid recommender systems of the last decade. It is the first quantitative review work completely focused in hybrid recommenders. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them. We also explore the hybridization classes each hybrid recommender belongs to, the application domains, the evaluation process and proposed future research directions. Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way. Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors. As most of the studies are evaluated by comparisons with similar methods using accuracy metrics, providing more credible and user oriented evaluations remains a typical challenge. Besides this, newer challenges were also identified such as responding to the variation of user context, evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid recommenders represent a good basis with which to respond accordingly by exploring newer opportunities such as contextualizing recommendations, involving parallel hybrid algorithms, processing larger datasets, etc.

Hybrid recommender systems: A systematic literature review

Hybrid Recommender Systems: A Systematic Literature Review

Music emotion recognition and recommendations today are changing the way people find and listen to their preferred musical tracks Emotion recognition of songs is mostly based on feature extraction and learning from available datasets In this work we take a different approach utilizing content words of lyrics and their valence and arousal norms in affect lexicons only We use this method to annotate each song with one of the four emotion categories of Russell's model, and also to construct MoodyLyrics, a large dataset of lyrics that will be available for public use For evaluation we utilized another lyrics dataset as ground truth and achieved an accuracy of 7425 % Our results confirm that valence is a better discriminator of mood than arousal The results also prove that music mood recognition or annotation can be achieved with good accuracy even without subjective human feedback or user tags, when they are not available

MoodyLyrics: A Sentiment Annotated Lyrics Dataset

Music emotion recognition today is based on techniques that require high quality and large emotionally labeled sets of songs to train algorithms. Manual and professional annotations of songs are costly and hardly accomplished. There is a high need for datasets that are public, highly polarized, large in size and following popular emotion representation models. In this paper we present the steps we followed to create two such datasets using intelligence of last.fm community tags. In the first dataset, songs are categorized based on an emotion space of four clusters we adopted from literature observations. The second dataset discriminates between positive and negative songs only. We also observed that last.fm mood tags are biased towards positive emotions. This imbalance of tags was reflected in cluster sizes of the resulting datasets we obtained; they contain more positive songs than negative ones.

/pdf/music-mood-dataset-creation-based-on-last-fm-tags-21bgoltzci.pdf

Music Mood Dataset Creation Based on Last.fm Tags

As Recommender Systems are becoming very common and widespread, there is an increasing need to evaluate their characteristics such as accuracy, diversity, scalability etc. One of the most fruitful ways to do this is by using public datasets with explicit user feedback about the items. In this paper we present and describe more than 20 available datasets covering different domains such as movies, books, music etc. Each dataset is described over a number of attributes such as size, domain, format of the data, type of access. Unfortunately we did not find any information about the quality of the data contained, that remains an open issue. We also refer to examples from the literature about using the datasets to evaluate recommendation algorithms or solutions. Overall aim of the paper is to offer a convenient resource for finding and selecting datasets as a support for the empirical evaluation of recommendation algorithms and techniques.

/pdf/characterization-of-public-datasets-for-recommender-systems-35lku8zkhn.pdf

Erion Çano

Papers

Hybrid recommender systems: A systematic literature review

Hybrid Recommender Systems: A Systematic Literature Review

MoodyLyrics: A Sentiment Annotated Lyrics Dataset

Music Mood Dataset Creation Based on Last.fm Tags

Characterization of public datasets for Recommender Systems