  Traditional sentiment construction in finance relies heavily on the
dictionary-based approach, with a few exceptions using simple machine learning
techniques such as Naive Bayes classifier. While the current literature has not
yet invoked the rapid advancement in the natural language processing, we
construct in this research a textual-based sentiment index using a well-known
pre-trained model BERT developed by Google, especially for three actively
trading individual stocks in Hong Kong market with at the same time the hot
discussion on Weibo.com. On the one hand, we demonstrate a significant
enhancement of applying BERT in financial sentiment analysis when compared with
the existing models. On the other hand, by combining with the other two
commonly-used methods when it comes to building the sentiment index in the
financial literature, i.e., the option-implied and the market-implied
approaches, we propose a more general and comprehensive framework for the
financial sentiment analysis, and further provide convincing outcomes for the
predictability of individual stock return by combining LSTM (with a feature of
a nonlinear mapping). It is significantly distinct with the dominating
econometric methods in sentiment influence analysis which are all of a nature
of linear regression.
