1,720,957 research outputs found
Sentiment Classification With Convolutional Neural Network using Multiple Word Representations
No full textMost neural network models for sentiment classification use word vectors pre-trained by word embedding methods to represent a word. Although word vectors are trained on large corpus, most of them are restricted by the vocabularies in the corpus. Since sentiment classification models have to capture subtle meaning of sentence, it is desirable to represent words that have not been pretrained by word embedding method. To achieve this goal, we propose a sentiment classification model with convolutional neural network using multipleword representations. We represent aword by three embedding methods including word2vec, GloVe, and our method which is based on a character level embedding method that successfully captures subtle differences between words. Experimental results from three datasets show that our model with an additional character level embedding method improves the accuracy of the sentiment classification. © 2018 ACM.1
DualSentiNet : Dual Prediction of Word and Document Sentiments Using Shared Word Embedding
No full textWith the popularization of social networking services, numerous words are newly emerging every day in personalized document sources. Slang terms, abbreviations, newly coined words, and nongrammatical words or expressions belong here, and people are more likely to use these words with a certain sentimental tendency compared to other standard words. Thus, it becomes important to nd their meanings or sentiments to analyze the sentiment of user-generated texts. This paper proposes a novel sentiment analysis model, termed DualSentiNet, which predicts the sentiments of newly emerged words and documents at the same time. Our model is composed of three parts: (i) a word-level sentiment regression network, (ii) a document-level sentiment classi cation network, and (iii) a shared word embedding layer. DualSentiNet makes a word embedding layer shared by two different networks, thereby learning richer information about both word-level and document-level sentiments through two-way back-propagation. Consequently, it improves the performance of sentiment prediction by preventing word vectors from being over tted. Experimental results show that DualSentiNet signi cantly outperforms competitors in terms of both document sentiment classi cation accuracy and the word sentiment regression RMSE. In addition, DualSentiNet produces better word embedding by reecting both word and document sentiments. © 2018 ACM.1
Bootstrapping User and Item Representations for One-Class Collaborative Filtering
No full textThe goal of one-class collaborative filtering (OCCF) is to identify the user-item pairs that are positively-related but have not been interacted yet, where only a small portion of positive user-item interactions (e.g., users’ implicit feedback) are observed. For discriminative modeling between positive and negative interactions, most previous work relied on negative sampling to some extent, which refers to considering unobserved user-item pairs as negative, as actual negative ones are unknown. However, the negative sampling scheme has critical limitations because it may choose “positive but unobserved” pairs as negative. This paper proposes a novel OCCF framework, named as BUIR, which does not require negative sampling. To make the representations of positively-related users and items similar to each other while avoiding a collapsed solution, BUIR adopts two distinct encoder networks that learn from each other; the first encoder is trained to predict the output of the second encoder as its target, while the second encoder provides the consistent targets by slowly approximating the first encoder. In addition, BUIR effectively alleviates the data sparsity issue of OCCF, by applying stochastic data augmentation to encoder inputs. Based on the neighborhood information of users and items, BUIR randomly generates the augmented views of each positive interaction each time it encodes, then further trains the model by this self-supervision. Our extensive experiments demonstrate that BUIR consistently and significantly outperforms all baseline methods by a large margin especially for much sparse datasets in which any assumptions about negative interactions are less valid
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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