Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A
  Natural Language Processing Approach
Yufei Xie Rodolfo C. Raga Jr
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.