Technology acceptance models effectively predict how users will adopt new
technology products. Traditional surveys, often expensive and cumbersome, are
commonly used for this assessment. As an alternative to surveys, we explore the
use of large language models for annotating online user-generated content, like
digital reviews and comments. Our research involved designing an LLM annotation
system that transform reviews into structured data based on the Unified Theory
of Acceptance and Use of Technology model. We conducted two studies to validate
the consistency and accuracy of the annotations. Results showed
moderate-to-strong consistency of LLM annotation systems, improving further by
lowering the model temperature. LLM annotations achieved close agreement with
human expert annotations and outperformed the agreement between experts for
UTAUT variables. These results suggest that LLMs can be an effective tool for
analyzing user sentiment, offering a practical alternative to traditional
survey methods and enabling deeper insights into technology design and
adoption.

利用大型语言模型进行注释，以转化评论为结构化数据，验证其在用户情感分析方面的有效性。

利用大型语言模型（LLM）注释系统扩展技术接受分析

Scaling Technology Acceptance Analysis with Large Language Model (LLM)  Annotation Systems

The massive collection of user posts across social media platforms is
primarily untapped for artificial intelligence (AI) use cases based on the
sheer volume and velocity of textual data. Natural language processing (NLP) is
a subfield of AI that leverages bodies of documents, known as corpora, to train
computers in human-like language understanding. Using a word ranking method,
term frequency-inverse document frequency (TF-IDF), to create features across
documents, it is possible to perform unsupervised analytics, machine learning
(ML) that can group the documents without a human manually labeling the data.
For large datasets with thousands of features, t-distributed stochastic
neighbor embedding (t-SNE), k-means clustering and Latent Dirichlet allocation
(LDA) are employed to learn top words and generate topics for a Reddit and
Twitter combined corpus. Using extremely simple deep learning models, this
study demonstrates that the applied results of unsupervised analysis allow a
computer to predict either negative, positive, or neutral user sentiment
towards plastic surgery based on a tweet or subreddit post with almost 90%
accuracy. Furthermore, the model is capable of achieving higher accuracy on the
unsupervised sentiment task than on a rudimentary supervised document
classification task. Therefore, unsupervised learning may be considered a
viable option in labeling social media documents for NLP tasks.

该研究使用社交媒体数据进行无监督分析，利用自然语言处理、人工智能和深度学习模型来预测用户对整形手术的情感倾向，取得了近 90％的准确率，证明无监督学习在社交媒体文档标记方面是可行的。

无监督式整形外科社交媒体帖子的情感分析

Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts

End-to-end learning framework is useful for building dialog systems for its
simplicity in training and efficiency in model updating. However, current
end-to-end approaches only consider user semantic inputs in learning and
under-utilize other user information. Therefore, we propose to include user
sentiment obtained through multimodal information (acoustic, dialogic and
textual), in the end-to-end learning framework to make systems more
user-adaptive and effective. We incorporated user sentiment information in both
supervised and reinforcement learning settings. In both settings, adding
sentiment information reduced the dialog length and improved the task success
rate on a bus information search task. This work is the first attempt to
incorporate multimodal user information in the adaptive end-to-end dialog
system training framework and attained state-of-the-art performance.

本研究提出，在末端到末端的对话系统学习框架中包含通过多模式信息获取的用户情感信息，以使系统更具用户适应性和效果。结果，这项工作是在自适应末端到末端对话系统训练框架中首次尝试将多模式用户信息纳入其中并达到最先进的表现。