Transformer Based Memory Network for Sentiment Analysis of Web Comments

The boom in wireless networking technology has led to an exponential increase in the number of web comments. Therefore, sentiment analysis of web comments is vital, and aspect-based sentiment analysis(ABSA) is very useful for the sentiment feature extraction of web comments. Currently, context-dependent sentiment feature typically derives from recurrent neural networks (RNN), and an average target vector usually replaces the target vector. However, web comments have become increasingly complex, and RNN may lose some essential sentiment information. At the same time, the average target vector may be the wrong target feature. We propose a new Transformer based memory network (TF-MN) to correct the shortcomings of the previous method. In TF-MN, the task becomes the question-answering process, which optimizes context, question, and the memory module. We use a global self-attention mechanism and a local attention mechanism (memory network) to construct emotionally inclined web comment semantics. Since self-attention can only obtain global semantic links, words such as nouns, prepositions, and adverbs still affect the emotional extraction of comments. To shield the influence of unrelated vocabulary on classification, we propose to use improved memory networks to optimize the extraction of web comments semantics. We conduct experiments on two datasets, and experimental results show that our model exceeds the state-of-the-art model.