Emotion analysis, which aims to determine the emotion contained in a piece of text via training a machine learning model, is a fine-grained sentiment analysis task. Emotion analysis can be divided into two tasks:Emotion classification and emotion regression. In this paper, an adversarial neural network is proposed for multi-dimensional emotion regression task. The proposed network consists of three modules:Feature extractors, regressors, and a discriminator. The network aims to train multiple feature extractors and regressors to score for multiple emotion dimensions for a textual input. Feature extractors take a text as inputs, and extract different feature vectors for different emotion dimensions. Regressors take extracted feature vectors as inputs to score for multiple emotion dimensions. The discriminator take an extracted feature vector as its input, and discriminate for which emotion dimension the feature vector is extracted. The proposed approach conducts adversarial training between different feature extractors via the discriminator in order to training feature extractors which can extract more generalized features for multiple emotion dimensions. Empirical studies on EMOBANK corpus demonstrate the notable improvements in r-value achieved by the proposed approach on EMOBANK readers' and writers' emotion regression in news domain and fictions domain compared to all baseline systems, including several state-of-the-art text regression systems.