  Leveraging short-text contents to estimate the occupation of microblog
authors has significant gains in many applications. Yet challenges abound.
Firstly brief textual contents come with excessive lexical noise that makes the
inference problem challenging. Secondly, cognitive-semantics are not evident,
and important linguistic features are latent in short-text contents. Thirdly,
it is hard to measure the correlation between the cognitive short-text
semantics and the features pertaining various occupations. We argue that the
multi-aspect cognitive features are needed to correctly associate short-text
contents to a particular job and discover suitable people for the careers. To
this end, we devise a novel framework that on the one hand, can infer
short-text contents and exploit cognitive features, and on the other hand,
fuses various adopted novel algorithms, such as curve fitting, support vector,
and boosting modules to better predict the occupation of the authors. The final
estimation module manufactures the $R^w$-tree via coherence weight to tune the
best outcome in the inferring process. We conduct comprehensive experiments on
real-life Twitter data. The experimental results show that compared to other
rivals, our cognitive multi-aspect model can achieve a higher performance in
the career estimation procedure, where it is inevitable to neglect the
contextual semantics of users.
