论文标题
基于深度学习的情感分析：比较研究
Sentiment Analysis Based on Deep Learning: A Comparative Study
论文作者
论文摘要
对公众舆论的研究可以为我们提供有价值的信息。在Twitter或Facebook等社交网络上对情感的分析已成为了解用户意见并具有广泛应用程序的强大手段。但是，自然语言处理（NLP）遇到的挑战阻碍了情感分析的效率和准确性。近年来，已经证明深度学习模型是应对NLP挑战的有前途的解决方案。本文回顾了采用深度学习来解决情感分析问题（例如情感极性）的最新研究。使用频率内文档频率（TF-IDF）和单词嵌入的模型已应用于一系列数据集。最后，已经对不同模型和输入特征获得的实验结果进行了比较研究
The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users' opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input features
