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Interview Data Analysis using Machine Learning Techniques to Predict Personality Traits

机译：采访数据分析，采用机器学习技术预测人格特征

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In this paper, we analyze the MIT Interview dataset and find the relation between the various prosodic features (such as intensity, pitch, frequency, etc.) and the likelihood of the person getting a virtuous assessment in the interview. These prosodic features help in rating a person on several grounds such as how engaging or excited or friendly the candidate was. We have demonstrated how selecting only a few of the prosodic features can give better prediction results. This was done by selecting the top features using the `recursive feature elimination' technique for five personality traits such as `Engaged', `Excited', `Friendly', `Calm' and `Speaking Rate'. It was found that for traits such as `Engaged' and `Excited', the prosodic features related to intensity play a major role. For personality trait `Friendly', prosodic features like pitch and duration of pause are more relevant. Similarly, for personality trait `Calm', prosodic features related to pitch play a major role and so on. Once the top features were selected, we applied three different regression models with tenfold cross validation to determine the best method for predicting these personality traits. These regression models were evaluated by calculating the negative mean squared error, coefficient of determination, etc. Based on the empirical results, decision tree proved to be the best method for predicting the personality traits based on the selected prosodic features.
机译：在本文中，我们分析了麻省理工学院访谈数据集，并找到各种韵律特征（如强度，俯仰，频率等）之间的关系，并且在面试中获得良性评估的人的可能性。这些韵律的特点有助于在几个场地评定一个人，例如如何参与或兴奋或友好候选人。我们已经证明了仅选择少数韵律功能可以提供更好的预测结果。这是通过选择使用“递归特征消除”技术的最高特征来完成的，这是用于五个个性特征，例如“订婚”，“兴奋”，“友好”，“平静”和“说话率”。有人发现，对于诸如“订婚”和“兴奋”之类的特征，与强度相关的韵律特征起着重要作用。对于个性特质“友好”，韵律特征像节距和暂停的持续时间更相关。同样，对于人格特质“平静”，与音高相关的韵律特征起到了重要作用等。选择顶部特征后，我们应用了三个不同的回归模型，具有十倍的交叉验证，以确定预测这些人格特征的最佳方法。通过计算基于经验结果的负平均平方误差，确定系数等来评估这些回归模型。被证明是基于所选择的韵律特征来预测人格性状的最佳方法。

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