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

TL;DR

This paper presents a CNN-based approach for sentiment analysis on Weibo tweets, achieving a macro F1-score of 0.73, demonstrating CNNs' effectiveness in social media NLP tasks with publicly available code for further research.

Contribution

The study introduces a CNN model trained on Weibo data with detailed preprocessing, providing a practical NLP approach and publicly sharing the experimental setup and code.

Findings

Achieved macro-average F1-score of 0.73

Balanced performance across sentiment categories

Publicly available code and dataset for replication

Abstract

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.