  Automatic text generation has received much attention owing to rapid
development of deep neural networks. In general, text generation systems based
on statistical language model will not consider anthropomorphic
characteristics, which results in machine-like generated texts. To fill the
gap, we propose a conditional language generation model with Big Five
Personality (BFP) feature vectors as input context, which writes human-like
short texts. The short text generator consists of a layer of long short memory
network (LSTM), where a BFP feature vector is concatenated as one part of input
for each cell. To enable supervised training generation model, a text
classification model based convolution neural network (CNN) has been used to
prepare BFP-tagged Chinese micro-blog corpora. Validated by a BFP linguistic
computational model, our generated Chinese short texts exhibit discriminative
personality styles, which are also syntactically correct and semantically
smooth with appropriate emoticons. With combination of natural language
generation with psychological linguistics, our proposed BFP-dependent text
generation model can be widely used for individualization in machine
translation, image caption, dialogue generation and so on.
