融合情感与语义信息的情感分析方法

在使用词嵌入法进行词转向量时,两个反义词会转换成相近的向量。如果这两个词是情感词,将会导致词的情感信息的丢失,这在情感分析任务中是不合理的。为了解决这个问题,提出了一种在词嵌入的基础上增加情感向量来获取情感信息的方法。首先利用情感词典资源构建情感向量,将其与词嵌入法得到的词向量融合在一起;然后采用双向长短期记忆(BiLSTM)网络获取文本的特征;最后对文本的情感进行分类。在4个数据集上分别对该方法与未融合情感向量的方法进行了实验。实验结果表明所提方法分类准确度与F1值都高于未融合方法,说明了加入情感向量有助于提高情感分析的性能。

Sentiment analysis method combining sentiment and semantic information

When using word embedding method for word-to-vector, two antonyms are converted into similar vectors. If they are sentiment words, it will lead to the loss of sentimental information, which is unreasonable in sentiment analysis task. To solve this problem, a method of adding sentiment vectors to obtain sentiment information based on word embedding was proposed. Firstly, the sentiment vector was constructed by using sentiment lexicon, and combined with word vector obtained by word embedding method. Then, a bidirectional Long Short Term Memory (BiLSTM) network was used to obtain the characteristics of text. Finally, the sentiment of text was classified. Experiments of the proposed method and the method without fusing sentimental vector were carried out on four datasets. The experimental results show that the classification accuracy and F1 score of the proposed method are higher than those of the method without fusion, which indicates that adding sentimental vectors is beneficial to improve the performance of sentiment analysis.