  [Purpose] To understand the meaning of a sentence, humans can focus on
important words in the sentence, which reflects our eyes staying on each word
in different gaze time or times. Thus, some studies utilize eye-tracking values
to optimize the attention mechanism in deep learning models. But these studies
lack to explain the rationality of this approach. Whether the attention
mechanism possesses this feature of human reading needs to be explored.
[Design/methodology/approach] We conducted experiments on a sentiment
classification task. Firstly, we obtained eye-tracking values from two
open-source eye-tracking corpora to describe the feature of human reading.
Then, the machine attention values of each sentence were learned from a
sentiment classification model. Finally, a comparison was conducted to analyze
machine attention values and eye-tracking values. [Findings] Through
experiments, we found the attention mechanism can focus on important words,
such as adjectives, adverbs, and sentiment words, which are valuable for
judging the sentiment of sentences on the sentiment classification task. It
possesses the feature of human reading, focusing on important words in
sentences when reading. Due to the insufficient learning of the attention
mechanism, some words are wrongly focused. The eye-tracking values can help the
attention mechanism correct this error and improve the model performance.
[Originality/value] Our research not only provides a reasonable explanation for
the study of using eye-tracking values to optimize the attention mechanism, but
also provides new inspiration for the interpretability of attention mechanism.
