School of Electronic Science and Engineering, Hunan University of Information Technology, Changsha, Hunan 410151, China.
Comput Intell Neurosci. 2022 Jun 29;2022:8208561. doi: 10.1155/2022/8208561. eCollection 2022.
The goal of Chinese fine-grained emotion analysis is to identify the target words corresponding to fine-grained elements from sentences and determine the corresponding emotional polarity for the target words. Aiming at the problem that the current Sina Microblog user emotion analysis methods have low accuracy and are difficult to effectively predict and manage, a Sina Microblog user emotion analysis method based on the Bidirectional Long Short-Term Memory algorithm (BiLSTM) and improved hierarchical attention mechanism is proposed. Firstly, an emotion analysis model is constructed based on text-level analysis and subjective and objective analysis, and the dimensionality curse problem of one-hot representation is solved by integrating the weighted word vector of TF-IDF. Then, by constructing a bidirectional long short-term memory neural network, the full acquisition of context information is realized, which increases the fine-grained elements of emotion analysis. Finally, by introducing an improved hierarchical attention mechanism, the network model can focus on different parts to achieve text classification and emotion analysis. Through simulation experiments, the proposed emotion analysis method and the other two methods are compared and analyzed under the condition of using the same database. The results show that the precision, recall, and 1 value of the method proposed in this paper are the best under 7 different emotion classifications, with the highest reaching 95.8%, 95.9%, and 96.1%, respectively, and the algorithm performance is better than the other two comparisons algorithm. It is proved that the proposed model has excellent performance.
中文细粒度情感分析的目标是从句子中识别与细粒度元素相对应的目标词，并确定目标词的相应情感极性。针对当前新浪微博用户情感分析方法准确率低、难以有效预测和管理的问题，提出了一种基于双向长短期记忆算法（BiLSTM）和改进层次注意力机制的新浪微博用户情感分析方法。首先，基于文本级分析和主客观分析构建情感分析模型，通过集成 TF-IDF 的加权词向量解决 one-hot 表示的维度诅咒问题。然后，通过构建双向长短期记忆神经网络，实现对上下文信息的充分获取，增加情感分析的细粒度元素。最后，通过引入改进的层次注意力机制，使网络模型能够关注不同的部分，实现文本分类和情感分析。通过仿真实验，在使用相同数据库的条件下，对所提出的情感分析方法与另外两种方法进行了比较分析。结果表明，在 7 种不同的情感分类下，本文提出的方法在精度、召回率和 1 值方面的表现最佳，最高分别达到 95.8%、95.9%和 96.1%，算法性能优于另外两种对比算法，证明了所提出的模型具有优异的性能。
