10,578 research outputs found
Numeral Understanding in Financial Tweets for Fine-grained Crowd-based Forecasting
Numerals that contain much information in financial documents are crucial for
financial decision making. They play different roles in financial analysis
processes. This paper is aimed at understanding the meanings of numerals in
financial tweets for fine-grained crowd-based forecasting. We propose a
taxonomy that classifies the numerals in financial tweets into 7 categories,
and further extend some of these categories into several subcategories. Neural
network-based models with word and character-level encoders are proposed for
7-way classification and 17-way classification. We perform backtest to confirm
the effectiveness of the numeric opinions made by the crowd. This work is the
first attempt to understand numerals in financial social media data, and we
provide the first comparison of fine-grained opinion of individual investors
and analysts based on their forecast price. The numeral corpus used in our
experiments, called FinNum 1.0 , is available for research purposes.Comment: Accepted by the 2018 IEEE/WIC/ACM International Conference on Web
Intelligence (WI 2018), Santiago, Chil
NumHG: A Dataset for Number-Focused Headline Generation
Headline generation, a key task in abstractive summarization, strives to
condense a full-length article into a succinct, single line of text. Notably,
while contemporary encoder-decoder models excel based on the ROUGE metric, they
often falter when it comes to the precise generation of numerals in headlines.
We identify the lack of datasets providing fine-grained annotations for
accurate numeral generation as a major roadblock. To address this, we introduce
a new dataset, the NumHG, and provide over 27,000 annotated numeral-rich news
articles for detailed investigation. Further, we evaluate five well-performing
models from previous headline generation tasks using human evaluation in terms
of numerical accuracy, reasonableness, and readability. Our study reveals a
need for improvement in numerical accuracy, demonstrating the potential of the
NumHG dataset to drive progress in number-focused headline generation and
stimulate further discussions in numeral-focused text generation.Comment: NumEval@SemEval-2024 Datase
A Stage For Social Comparison — The Value Of Information In Virtual Communities
Virtual communities have become significant applica tions for the Internet. Previous studies usually treated virtual communities as places for people to share and exchange information and did not explain the social value of comm unities well. This study treated a virtual community as a stage on which people can present themselves to other users while others can see the shows of people to satisfy their social comparison needs. Based on social co mparison theory, this paper investigated the effects of upward social comparison in virtual communiti es on user satisfaction through the mediations of perceived inspiration and self-improvement. Furthermore, these effects were moderated by individual social comparison orientation. The results of this study should enhance the understanding of the nature and the social value of information in virtual communities
Intrinsic Alignment in redMaPPer clusters -- II. Radial alignment of satellites toward cluster centers
We study the orientations of satellite galaxies in redMaPPer clusters
constructed from the Sloan Digital Sky Survey at to determine
whether there is any preferential tendency for satellites to point radially
toward cluster centers. We analyze the satellite alignment (SA) signal based on
three shape measurement methods (re-Gaussianization, de Vaucouleurs, and
isophotal shapes), which trace galaxy light profiles at different radii. The
measured SA signal depends on these shape measurement methods. We detect the
strongest SA signal in isophotal shapes, followed by de Vaucouleurs shapes.
While no net SA signal is detected using re-Gaussianization shapes across the
entire sample, the observed SA signal reaches a statistically significant level
when limiting to a subsample of higher luminosity satellites. We further
investigate the impact of noise, systematics, and real physical isophotal
twisting effects in the comparison between the SA signal detected via different
shape measurement methods. Unlike previous studies, which only consider the
dependence of SA on a few parameters, here we explore a total of 17 galaxy and
cluster properties, using a statistical model averaging technique to naturally
account for parameter correlations and identify significant SA predictors. We
find that the measured SA signal is strongest for satellites with the following
characteristics: higher luminosity, smaller distance to the cluster center,
rounder in shape, higher bulge fraction, and distributed preferentially along
the major axis directions of their centrals. Finally, we provide physical
explanations for the identified dependences, and discuss the connection to
theories of SA.Comment: 25 pages, 16 figures, 7 tables, accepted to MNRAS. Main statistical
analysis tool changed, with the results remain simila
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