论文标题
具有深度学习模型的情感分析：关于僧伽罗语言十年的比较研究Facebook数据
Sentiment Analysis with Deep Learning Models: A Comparative Study on a Decade of Sinhala Language Facebook Data
论文作者
论文摘要
Facebook帖子与相应的反应功能之间的关系是一个有趣的主题，可以探索和理解。为了实现这一目标，我们针对包含数百万个反应的十年来的数据集测试了最先进的僧伽罗情绪分析模型。为了建立基准，为了确定僧伽罗情绪分析的最佳模型，我们还测试了相同的数据集配置，其他深度学习模型迎合了情感分析。在这项研究中，我们报告说，三层双向LSTM模型的F1得分为Sinhala情感分析的84.58％，超过了当前的最新模型。胶囊B，仅设法获得F1分数为82.04％。此外，由于所有深度学习模型都显示F1得分高于75％，因此我们得出结论，可以肯定地声称Facebook反应适合预测文本的情感。
The relationship between Facebook posts and the corresponding reaction feature is an interesting subject to explore and understand. To achieve this end, we test state-of-the-art Sinhala sentiment analysis models against a data set containing a decade worth of Sinhala posts with millions of reactions. For the purpose of establishing benchmarks and with the goal of identifying the best model for Sinhala sentiment analysis, we also test, on the same data set configuration, other deep learning models catered for sentiment analysis. In this study we report that the 3 layer Bidirectional LSTM model achieves an F1 score of 84.58% for Sinhala sentiment analysis, surpassing the current state-of-the-art model; Capsule B, which only manages to get an F1 score of 82.04%. Further, since all the deep learning models show F1 scores above 75% we conclude that it is safe to claim that Facebook reactions are suitable to predict the sentiment of a text.
