104,411 research outputs found
Author Age Prediction from Text using Linear Regression
While the study of the connection between discourse patterns and personal identification is decades old, the study of these patterns using language technologies is relatively recent. In that more recent tradition we frame author age prediction from text as a regression problem. We explore the same task using three very different genres of data simultaneously: blogs, telephone conversations, and online forum posts. We employ a technique from domain adaptation that allows us to train a joint model involving all three corpora together as well as separately and analyze differences in predictive features across joint and corpusspecific aspects of the model. Effective features include both stylistic ones (such as POS patterns) as well as content oriented ones. Using a linear regression model based on shallow text features, we obtain correlations up to 0.74 and mean absolute errors between 4.1 and 6.8 years.
Understanding the Impact of Early Citers on Long-Term Scientific Impact
This paper explores an interesting new dimension to the challenging problem
of predicting long-term scientific impact (LTSI) usually measured by the number
of citations accumulated by a paper in the long-term. It is well known that
early citations (within 1-2 years after publication) acquired by a paper
positively affects its LTSI. However, there is no work that investigates if the
set of authors who bring in these early citations to a paper also affect its
LTSI. In this paper, we demonstrate for the first time, the impact of these
authors whom we call early citers (EC) on the LTSI of a paper. Note that this
study of the complex dynamics of EC introduces a brand new paradigm in citation
behavior analysis. Using a massive computer science bibliographic dataset we
identify two distinct categories of EC - we call those authors who have high
overall publication/citation count in the dataset as influential and the rest
of the authors as non-influential. We investigate three characteristic
properties of EC and present an extensive analysis of how each category
correlates with LTSI in terms of these properties. In contrast to popular
perception, we find that influential EC negatively affects LTSI possibly owing
to attention stealing. To motivate this, we present several representative
examples from the dataset. A closer inspection of the collaboration network
reveals that this stealing effect is more profound if an EC is nearer to the
authors of the paper being investigated. As an intuitive use case, we show that
incorporating EC properties in the state-of-the-art supervised citation
prediction models leads to high performance margins. At the closing, we present
an online portal to visualize EC statistics along with the prediction results
for a given query paper
Distributed multinomial regression
This article introduces a model-based approach to distributed computing for
multinomial logistic (softmax) regression. We treat counts for each response
category as independent Poisson regressions via plug-in estimates for fixed
effects shared across categories. The work is driven by the
high-dimensional-response multinomial models that are used in analysis of a
large number of random counts. Our motivating applications are in text
analysis, where documents are tokenized and the token counts are modeled as
arising from a multinomial dependent upon document attributes. We estimate such
models for a publicly available data set of reviews from Yelp, with text
regressed onto a large set of explanatory variables (user, business, and rating
information). The fitted models serve as a basis for exploring the connection
between words and variables of interest, for reducing dimension into supervised
factor scores, and for prediction. We argue that the approach herein provides
an attractive option for social scientists and other text analysts who wish to
bring familiar regression tools to bear on text data.Comment: Published at http://dx.doi.org/10.1214/15-AOAS831 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Testing Market Response to Auditor Change Filings: a comparison of machine learning classifiers
The use of textual information contained in company filings with the Securities Exchange Commission (SEC), including annual reports on Form 10-K, quarterly reports on Form 10-Q, and current reports on Form 8-K, has gained the increased attention of finance and accounting researchers. In this paper we use a set of machine learning methods to predict the market response to changes in a firm\u27s auditor as reported in public filings. We vectorize the text of 8-K filings to test whether the resulting feature matrix can explain the sign of the market response to the filing. Specifically, using classification algorithms and a sample consisting of the Item 4.01 text of 8-K documents, which provides information on changes in auditors of companies that are registered with the SEC, we predict the sign of the cumulative abnormal return (CAR) around 8-K filing dates. We report the correct classification performance and time efficiency of the classification algorithms. Our results show some improvement over the naïve classification method
What Twitter Profile and Posted Images Reveal About Depression and Anxiety
Previous work has found strong links between the choice of social media
images and users' emotions, demographics and personality traits. In this study,
we examine which attributes of profile and posted images are associated with
depression and anxiety of Twitter users. We used a sample of 28,749 Facebook
users to build a language prediction model of survey-reported depression and
anxiety, and validated it on Twitter on a sample of 887 users who had taken
anxiety and depression surveys. We then applied it to a different set of 4,132
Twitter users to impute language-based depression and anxiety labels, and
extracted interpretable features of posted and profile pictures to uncover the
associations with users' depression and anxiety, controlling for demographics.
For depression, we find that profile pictures suppress positive emotions rather
than display more negative emotions, likely because of social media
self-presentation biases. They also tend to show the single face of the user
(rather than show her in groups of friends), marking increased focus on the
self, emblematic for depression. Posted images are dominated by grayscale and
low aesthetic cohesion across a variety of image features. Profile images of
anxious users are similarly marked by grayscale and low aesthetic cohesion, but
less so than those of depressed users. Finally, we show that image features can
be used to predict depression and anxiety, and that multitask learning that
includes a joint modeling of demographics improves prediction performance.
Overall, we find that the image attributes that mark depression and anxiety
offer a rich lens into these conditions largely congruent with the
psychological literature, and that images on Twitter allow inferences about the
mental health status of users.Comment: ICWSM 201
Early hospital mortality prediction using vital signals
Early hospital mortality prediction is critical as intensivists strive to
make efficient medical decisions about the severely ill patients staying in
intensive care units. As a result, various methods have been developed to
address this problem based on clinical records. However, some of the laboratory
test results are time-consuming and need to be processed. In this paper, we
propose a novel method to predict mortality using features extracted from the
heart signals of patients within the first hour of ICU admission. In order to
predict the risk, quantitative features have been computed based on the heart
rate signals of ICU patients. Each signal is described in terms of 12
statistical and signal-based features. The extracted features are fed into
eight classifiers: decision tree, linear discriminant, logistic regression,
support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and
K-nearest neighborhood (K-NN). To derive insight into the performance of the
proposed method, several experiments have been conducted using the well-known
clinical dataset named Medical Information Mart for Intensive Care III
(MIMIC-III). The experimental results demonstrate the capability of the
proposed method in terms of precision, recall, F1-score, and area under the
receiver operating characteristic curve (AUC). The decision tree classifier
satisfies both accuracy and interpretability better than the other classifiers,
producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It
indicates that heart rate signals can be used for predicting mortality in
patients in the ICU, achieving a comparable performance with existing
predictions that rely on high dimensional features from clinical records which
need to be processed and may contain missing information.Comment: 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE
2018 and published in Smart Health journa
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Decision trees in epidemiological research
Background:
In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However, identifying relevant subgroups can be challenging with standard statistical methods.
Main text:
We review the literature on decision trees, a family of techniques for partitioning the population, on the basis of covariates, into distinct subgroups who share similar values of an outcome variable. We compare two decision tree methods, the popular Classification and Regression tree (CART) technique and the newer Conditional Inference tree (CTree) technique, assessing their performance in a simulation study and using data from the Box Lunch Study, a randomized controlled trial of a portion size intervention. Both CART and CTree identify homogeneous population subgroups and offer improved prediction accuracy relative to regression-based approaches when subgroups are truly present in the data. An important distinction between CART and CTree is that the latter uses a formal statistical hypothesis testing framework in building decision trees, which simplifies the process of identifying and interpreting the final tree model. We also introduce a novel way to visualize the subgroups defined by decision trees. Our novel graphical visualization provides a more scientifically meaningful characterization of the subgroups identified by decision trees.
Conclusions:
Decision trees are a useful tool for identifying homogeneous subgroups defined by combinations of individual characteristics. While all decision tree techniques generate subgroups, we advocate the use of the newer CTree technique due to its simplicity and ease of interpretation
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