Title: What we really want to find by Sentiment Analysis: The Relationship between Computational Models and Psychological State,
Abstract: As the first step to model emotional state of a person, we build sentiment
analysis models with existing deep neural network algorithms and compare the
models with psychological measurements to enlighten the relationship. In the
experiments, we first examined psychological state of 64 participants and asked
them to summarize the story of a book, Chronicle of a Death Foretold (Marquez,
1981). Secondly, we trained models using crawled 365,802 movie review data;
then we evaluated participants' summaries using the pretrained model as a
concept of transfer learning. With the background that emotion affects on
memories, we investigated the relationship between the evaluation score of the
summaries from computational models and the examined psychological
measurements. The result shows that although CNN performed the best among other
deep neural network algorithms (LSTM, GRU), its results are not related to the
psychological state. Rather, GRU shows more explainable results depending on
the psychological state. The contribution of this paper can be summarized as
follows: (1) we enlighten the relationship between computational models and
psychological measurements. (2) we suggest this framework as objective methods
to evaluate the emotion; the real sentiment analysis of a person.