Short-Text Sentiment Classification Based on Graph-LSTM
Yuting Wan · 2019
With the rapid development of social networks, short texts make it easier for people to comment on hot events and express their emotions. The information published by users through short texts contains emotional features of different trends, and it is of great significance to dig deep into these features. However, the traditional RNN algorithm, SVM and linear LSTM can only discriminate emotional sentiment because of the short text grammar and the sparse data, which is far from the purpose of opinion mining. Therefore, this paper proposes to apply Graph LSTM to short text classification, mine deeper information, and achieve good results. Finally, the paper compares three different machine learning methods to achieve fine-grained sentiment analysis.