Emotional Intensity Estimation based on Writer’s Personality (2022)

Abstract: We propose a method for personalized emotional intensity estimation based on a writer’s personality test for Japanese SNS posts. Existing emotion analysis models are difficult to accurately estimate the writer’s subjective emotions behind the text. We personalize the emotion analysis using not only the text but also the writer’s personality information. Experimental results show that personality information improves the performance of emotional intensity estimation. Furthermore, a hybrid model combining the existing personalized method with ours achieved state-of-the-art performance.

## introduction emotional intensity estimation @xcite @xcite is one of the major challenges in the natural language processing community with many applications in dialogue systems @xcite and social media mining @xcite . emotional intensity estimation predicts the (often discretized) intensities of finer-grained emotions, such as ekman's basic emotions, i.e., joy, sadness, surprise, anger, fear, and disgust @xcite ) and plutchik's basic emotions, i.e., joy, sadness, expectation, surprise, anger, fear, disgust, and trust @xcite . wrime 1 @xcite is a corpus from social networking service (sns) text in japanese for emotional intensity estimation. as exemplified in a straightforward hypothesis to explain the difference is that the writer's personality affects their writing. this hypothesis seems plausible as the same text can have different meanings depending on who wrote it, the contexts such as the writer's preceding sns text and the circumstance the writer is in, etc; the writer's personality can influence all these aspects and can alter how they author text. this hypothesis inspires us to design a model specialized for subjective emotion. the model uses the personality test result of each writer, which is fortunately included in the corpus. specifically, given the personality test result, which is answers to 60 questions @xcite based on the big five personality five-factor model (goldberg, 1992), we embed 60 answers into a highdimensional feature vector. our model, shown in figure 1 combines feature vectors from the sns text and the personality to improve the estimation performance. i often have expectations for people i meet in real life, not on the web, and am a little disappointed due to overly high expectations. what should i do? why can't people who work hard be rewarded for their efforts? it's so frustrating. the upper rows of each table show examples of emotional intensity labels, consisting of subjective and objective ones, where three annotators (a-c) were invited for this sample (0: none, 1: weak, 2: medium, 3: strong). the lower part of each table shows the prediction results of the baseline model and our method. experimental results on the wrime corpus show that our model performs better than both bagof-words (bow) and bert @xcite baselines without personality information, which suggests the advantage of using writers' personality information for subjective emotional intensity estimation. furthermore, a hybrid model combining the existing personalized method @xcite with ours achieved state-of-the-art performance in emotional intensity estimation. the performance is on par with the performance of our human annotators. ## related work human emotions are subjective and have personal biases depending on many factors such as the first language, age, education @xcite , gender @xcite , race @xcite , and personality @xcite . due to the nature of such personal biases, writers may express different emotions even if they wrote the same text @xcite . taking into account the emotional differences between writers is important for a highquality emotional analysis. personalized emotion analysis has been studied in recent years. @xcite personalized the emotion analysis by focusing on the labeling variation among annotators. they proposed personal emotional bias (peb) as a measure of labeling variation and showed that such user-specific information contributes to emotional intensity estimation. @xcite personalized the emotion analysis by focusing on the personality of the text writer. they considered personality information based on the big five personality fivefactor model @xcite in a simple way (concatenation or attention) and showed that such user-specific information contributes to emotional intensity estimation. this study advances the latter approach and proposes a more effective method to model personality information for this task. ## methods as argued in section 1, we hypothesize that the writer's personality influences how they express themselves. we thus propose to leverage the personality of the writer as auxiliary information, especially for subjective emotional intensity estimation. figure 1 shows the overall structure of our model, which consists of the text stream and personality stream, fused together for estimating personalityaware emotional intensities. the text stream is the feature extractor of a basic emotional intensity estimation model, and the personality stream can also be seen as the feature extractor of regression model, trained to predict individual personality traits of the big five taxonomy (goldberg, 1992). ## experiments using wrime @xcite , a corpus for estimating the emotion analysis in japanese, we conduct an experiment to evaluate a four-class (i.e. none, weak, medium, and strong) classification of writers' emotional intensity. ## conclusions to improve the performance of estimating subjective emotional intensity by writers, we propose an emotional intensity estimation model that takes into account the writer's personality information. in the proposed method, we first extracted feature representations from the results of a personality test based on the big five personality five-factor model. then, we fused that personality features with textual features from bow or bert to personalize the emotional intensity estimation. experimental results on subjective emotional intensity estimation in japanese sns text reveal the effectiveness of the proposed methods in taking into account the personality of the writer. currently, our method requires writers to answer a 60-item personality test. therefore, our future work includes studying methods for estimating the writer's personality from their past posts, and how to combine them with the present method.