1,720,957 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Get PDFWe 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
Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media
No full textSentiment Analysis tools allow decision-makers to monitor changes of opinions on social
media towards entities, events, products, solutions, and services. These tools provide dashboards for tracking
positive, negative, and neutral sentiments for platforms like Twitter where millions of users express their
opinions on various topics. However, so far, these tools do not automatically extract reasons for sentiment
variations, and that makes it difficult to conclude necessary actions by decision-makers. In this paper,
we first compare performance of various Sentiment Analysis classifiers for short texts to select the top
performer. Then we present a Filtered-LDA framework that significantly outperformed existing methods
of interpreting sentiment variations on Twitter. The framework utilizes cascaded LDA Models with multiple
settings of hyperparameters to capture candidate reasons that cause sentiment changes. Then it applies a
filter to remove tweets that discuss old topics, followed by a Topic Model with a high Coherence Score to
extract Emerging Topics that are interpretable by a human. Finally, a novel Twitter’s sentiment reasoning
dashboard is introduced to display the most representative tweet for each candidate reason
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
Emerging Research Topic Detection Using Filtered-LDA
No full textComparing two sets of documents to identify new topics is useful in many applications, like
discovering trending topics from sets of scientific papers, emerging topic detection in microblogs, and
interpreting sentiment variations in Twitter. In this paper, the main topic-modeling-based approaches
to address this task are examined to identify limitations and necessary enhancements. To overcome
these limitations, we introduce two separate frameworks to discover emerging topics through a
filtered latent Dirichlet allocation (filtered-LDA) model. The model acts as a filter that identifies old
topics from a timestamped set of documents, removes all documents that focus on old topics, and
keeps documents that discuss new topics. Filtered-LDA also genuinely reduces the chance of using
keywords from old topics to represent emerging topics. The final stage of the filter uses multiple
topic visualization formats to improve human interpretability of the filtered topics, and it presents
the most-representative document for each topic
A Survey on Opinion Reason Mining and Interpreting Sentiment Variations
No full textTracking social media sentiment on a desired target is certainly an important query for many
decision-makers in fields like services, politics, entertainment, manufacturing, etc. As a result, there has been
a lot of focus on Sentiment Analysis. Moreover, some studies took one step ahead by analyzing subjective
texts further to understand possible motives behind extracted sentiments. Few other studies took several
steps ahead by attempting to automatically interpret sentiment variations. Learning reasons from sentiment
variations is indeed valuable, to either take necessary actions in a timely manner or learn lessons from
archived data. However, machines are still immature to carry out the full Sentiment Variations’ Reasoning
task perfectly due to various technical hurdles. This paper attempts to explore main approaches to Opinion
Reason Mining, with focus on Interpreting Sentiment Variations. Our objectives are investigating various
methods for solving the Sentiment Variations’ Reasoning problem and identifying some empirical research
gaps. To identify these gaps, a real-life Twitter dataset is analyzed, and key hypothesis for interpreting public
sentiment variations are examined
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