用改进的LSTM模型提升商业情感分析准确率至91.33%
A Long Short-Term Memory (LSTM) Model for Business Sentiment Analysis Based on Recurrent Neural Network
- 采用改进LSTM缓解梯度消失，替代传统RNN
- 在70%训练集上达到91.33%准确率
- 适合电商和企业用于产品反馈分析
商业情感分析（BSA）是自然语言处理的重要方向，旨在识别商业场景下的用户情绪。本文采用长短期记忆网络（LSTM）构建改进的循环神经网络模型，以克服传统RNN的梯度消失问题。实验基于产品评论数据集，使用70%数据训练，30%数据测试。结果表明，该模型相比传统RNN表现更优，准确率达到91.33%。该方法可帮助企业和电商平台分析客户对产品的偏好与不满，评估营销策略效果。
原文摘要 · Abstract (English)
Business sentiment analysis (BSA) is one of the significant and popular topics of natural language processing. It is one kind of sentiment analysis techniques for business purposes. Different categories of sentiment analysis techniques like lexicon-based techniques and different types of machine learning algorithms are applied for sentiment analysis on different languages like English, Hindi, Spanish, etc. In this paper, long short-term memory (LSTM) is applied for business sentiment analysis, where a recurrent neural network is used. An LSTM model is used in a modified approach to prevent the vanishing gradient problem rather than applying the conventional recurrent neural network (RNN). To apply the modified RNN model, product review dataset is used. In this experiment, 70\% of the data is trained for the LSTM and the rest 30\% of the data is used for testing. The result of this modified RNN model is compared with other conventional RNN models, and a comparison is made among the results. It is noted that the proposed model performs better than the other conventional RNN models. Here, the proposed model, i.e., the modified RNN model approach has achieved around 91.33\% of accuracy. By applying this model, any business company or e-commerce business site can identify the feedback from their customers about different types of products that customers like or dislike. Based on the customer reviews, a business company or e-commerce platform can evaluate its marketing strategy.
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