11,119 research outputs found
Econometrics meets sentiment : an overview of methodology and applications
The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software
Contextual Motifs: Increasing the Utility of Motifs using Contextual Data
Motifs are a powerful tool for analyzing physiological waveform data.
Standard motif methods, however, ignore important contextual information (e.g.,
what the patient was doing at the time the data were collected). We hypothesize
that these additional contextual data could increase the utility of motifs.
Thus, we propose an extension to motifs, contextual motifs, that incorporates
context. Recognizing that, oftentimes, context may be unobserved or
unavailable, we focus on methods to jointly infer motifs and context. Applied
to both simulated and real physiological data, our proposed approach improves
upon existing motif methods in terms of the discriminative utility of the
discovered motifs. In particular, we discovered contextual motifs in continuous
glucose monitor (CGM) data collected from patients with type 1 diabetes.
Compared to their contextless counterparts, these contextual motifs led to
better predictions of hypo- and hyperglycemic events. Our results suggest that
even when inferred, context is useful in both a long- and short-term prediction
horizon when processing and interpreting physiological waveform data.Comment: 10 pages, 7 figures, accepted for oral presentation at KDD '1
Recruitment Market Trend Analysis with Sequential Latent Variable Models
Recruitment market analysis provides valuable understanding of
industry-specific economic growth and plays an important role for both
employers and job seekers. With the rapid development of online recruitment
services, massive recruitment data have been accumulated and enable a new
paradigm for recruitment market analysis. However, traditional methods for
recruitment market analysis largely rely on the knowledge of domain experts and
classic statistical models, which are usually too general to model large-scale
dynamic recruitment data, and have difficulties to capture the fine-grained
market trends. To this end, in this paper, we propose a new research paradigm
for recruitment market analysis by leveraging unsupervised learning techniques
for automatically discovering recruitment market trends based on large-scale
recruitment data. Specifically, we develop a novel sequential latent variable
model, named MTLVM, which is designed for capturing the sequential dependencies
of corporate recruitment states and is able to automatically learn the latent
recruitment topics within a Bayesian generative framework. In particular, to
capture the variability of recruitment topics over time, we design hierarchical
dirichlet processes for MTLVM. These processes allow to dynamically generate
the evolving recruitment topics. Finally, we implement a prototype system to
empirically evaluate our approach based on real-world recruitment data in
China. Indeed, by visualizing the results from MTLVM, we can successfully
reveal many interesting findings, such as the popularity of LBS related jobs
reached the peak in the 2nd half of 2014, and decreased in 2015.Comment: 11 pages, 30 figure, SIGKDD 201
Basic tasks of sentiment analysis
Subjectivity detection is the task of identifying objective and subjective
sentences. Objective sentences are those which do not exhibit any sentiment.
So, it is desired for a sentiment analysis engine to find and separate the
objective sentences for further analysis, e.g., polarity detection. In
subjective sentences, opinions can often be expressed on one or multiple
topics. Aspect extraction is a subtask of sentiment analysis that consists in
identifying opinion targets in opinionated text, i.e., in detecting the
specific aspects of a product or service the opinion holder is either praising
or complaining about
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