{'fraction_non_alphanumeric': 0.01728201099764336, 'fraction_numerical': 0.002356637863315004, 'mean_word_length': 5.066666666666666, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A text sentiment analysis model based on self-attention mechanism

This paper focuses on the problem of text sentiment analysis. The task of sentiment analysis is to extract structured and valuable information from the text data that people express on various platforms. The field of sentiment analysis is attracting more and more attention from researchers. In recent years, due to the continuous development of deep learning theory, there have been many researches on the application of neural network model in sentiment analysis task. Sentiment analysis can be understood as the process of dividing text into different types according to the sentiment information expressed in the text. In this paper, we introduce the self-attention mechanism into sentiment analysis and propose a neural network model based on multi-head self-attention mechanism. In order to better capture the effective information in the text, we combine bidirectional GRU in our model. We evaluate our method on the dataset IMDB (Internet Movie Database). Experimental results show that the accuracy of the proposed model achieves 90.0 on the test dataset. It illuminates that our model outperforms other models. Our model can extract information more effectively in the task of sentiment analysis.