This study addressed the complex task of sentiment analysis on a dataset of
119,988 original tweets from Weibo using a Convolutional Neural Network (CNN),
offering a new approach to Natural Language Processing (NLP). The data, sourced
from Baidu's PaddlePaddle AI platform, were meticulously preprocessed,
tokenized, and categorized based on sentiment labels. A CNN-based model was
utilized, leveraging word embeddings for feature extraction, and trained to
perform sentiment classification. The model achieved a macro-average F1-score
of approximately 0.73 on the test set, showing balanced performance across
positive, neutral, and negative sentiments. The findings underscore the
effectiveness of CNNs for sentiment analysis tasks, with implications for
practical applications in social media analysis, market research, and policy
studies. The complete experimental content and code have been made publicly
available on the Kaggle data platform for further research and development.
Future work may involve exploring different architectures, such as Recurrent
Neural Networks (RNN) or transformers, or using more complex pre-trained models
like BERT, to further improve the model's ability to understand linguistic
nuances and context.

本研究采用卷积神经网络（CNN）对来自微博的 119,988 篇原始推文进行情感分析，利用词嵌入进行特征提取，并在情感标签的基础上训练模型执行情感分类，实现了对积极、中立和消极情绪的平衡分类，展示了 CNN 在情感分析任务中的有效性。

基于卷积神经网络的微博情感分析：自然语言处理方法

Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A  Natural Language Processing Approach

Market research surveys are a powerful methodology for understanding consumer
perspectives at scale, but are limited by depth of understanding and insights.
A virtual moderator can introduce elements of qualitative research into
surveys, developing a rapport with survey participants and dynamically asking
probing questions, ultimately to elicit more useful information for market
researchers. In this work, we introduce ${\tt SmartProbe}$, an API which
leverages the adaptive capabilities of large language models (LLMs), and
incorporates domain knowledge from market research, in order to generate
effective probing questions in any market research survey. We outline the
modular processing flow of $\tt SmartProbe$, and evaluate the quality and
effectiveness of its generated probing questions. We believe our efforts will
inspire industry practitioners to build real-world applications based on the
latest advances in LLMs. Our demo is publicly available at
this https URL

本文介绍了一种使用大语言模型的应用程序 SmartProbe, 它可以结合市场调研领域的知识，在任何市场调研中生成有效的深入问题，以获取更有用的信息。