432 research outputs found
Exploring the Public Perception in Social Big Data: An Investigation in Mars Recall Scandal
Social media has become a popular platform of interpersonal communication in which users can search for news and convey real-time information. Researching into social big data, such as Twitter, can be an effective way to identify public opinions and feelings in risk emergence, as it provides rich sources of data for opinion mining and sentiment analysis. This study aims to investigate and analyse the public perception towards the Mars and Snickers product recall scandal. The study proposes a comprehensive data analysis framework, and utilises the dataset formed of 10,930 Twitter messages over the span of 10-day following the product recall announcement made by Mars Inc., to gauge public attitudes and opinions. The study finds that the overall attitude of Twitter users towards the scandal was negative, and Snickers were the most mentioned product in the 10-day periods after the announcement of the recall. The data analysis highlights that the Tweet diffusion (retweeting) has positive associations with the number of followers and the use of hashtags, hence companies should pay more attention to users who have a large number of followers, as their tweets will be read by a great number of other Twitter users. The findings suggest effective methods for practitioners in crisis management (e.g., how to use social media to disseminate information). The study also presents a progressive tweet-mining framework that can serve as a tool in crisis management to classify the tweet topics, identify and analyse the sentiment and comprehend the changes of the public attitudes
Solar-type Stars Observed by LAMOST and Kepler
Obtaining measurements of chromospheric and photometric activity of stars
with near-solar fundamental parameters and rotation periods is important for a
better understanding of solar-stellar connection. We select a sample of 2603
stars with near-solar fundamental parameters from the Large Sky Area
Multi-Object Fiber Spectroscopic Telescope (LAMOST)-Kepler field and use LAMOST
spectra to measure their chromospheric activity and Kepler light curves to
measure their photospheric activity (i.e., the amplitude of the photometric
variability). While the rotation periods of 1556 of these stars could not be
measured due to the low amplitude of the photometric variability and highly
irregular temporal profile of light curves, 254 stars were further identified
as having near-solar rotation periods. We show that stars with near-solar
rotation periods have chromospheric activities that are systematically higher
than stars with undetected rotation periods. Furthermore, while the solar level
of photospheric and chromospheric activity appears to be typical for stars with
undetected rotation periods, the Sun appears to be less active than most stars
with near-solar rotation periods (both in terms of photospheric and
chromospheric activity).Comment: 7 pages, 6 figure
API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
Recent research has demonstrated that Large Language Models (LLMs) can
enhance their capabilities by utilizing external tools. However, three pivotal
questions remain unanswered: (1) How effective are current LLMs in utilizing
tools? (2) How can we enhance LLMs' ability to utilize tools? (3) What
obstacles need to be overcome to leverage tools? To address these questions, we
introduce API-Bank, a groundbreaking benchmark, specifically designed for
tool-augmented LLMs. For the first question, we develop a runnable evaluation
system consisting of 73 API tools. We annotate 314 tool-use dialogues with 753
API calls to assess the existing LLMs' capabilities in planning, retrieving,
and calling APIs. For the second question, we construct a comprehensive
training set containing 1,888 tool-use dialogues from 2,138 APIs spanning 1,000
distinct domains. Using this dataset, we train Lynx, a tool-augmented LLM
initialized from Alpaca. Experimental results demonstrate that GPT-3.5 exhibits
improved tool utilization compared to GPT-3, while GPT-4 excels in planning.
However, there is still significant potential for further improvement.
Moreover, Lynx surpasses Alpaca's tool utilization performance by more than 26
pts and approaches the effectiveness of GPT-3.5. Through error analysis, we
highlight the key challenges for future research in this field to answer the
third question.Comment: EMNLP 202
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