LSTM-Based Text Sentiment Analysis via Attention Mechanism

As an important platform for people to express their opinions, social networks have become a research hotspot of text sentiment analysis. This paper proposes an LSTM model based on the attention mechanism. The Iphone 10 introduction event comments on Twitter were taken as the research object, and the twitter users emotional tendency towards the event is analyzed to verify the validity of the model. Introduce deep learning theory, use the LSTM model based on attention mechanism to conduct sentiment analysis to better grasp the emotional information in the text and improve the success rate of sentiment classification. The LSTM model based on the attention mechanism is an effective model. The results show that it is more accurate when analyzing the emotional features of longer texts, and is more suitable for text analysis of small paragraphs.

کلیدواژه ها:

Natural Language Processing ، Sentiment Analysis ، Emotional Words ، Attention Mechanism ، LSTM Model