  The fusion of public sentiment data in the form of text with stock price
prediction is a topic of increasing interest within the financial community.
However, the research literature seldom explores the application of investor
sentiment in the Portfolio Selection problem. This paper aims to unpack and
develop an enhanced understanding of the sentiment aware portfolio selection
problem. To this end, the study uses a Semantic Attention Model to predict
sentiment towards an asset. We select the optimal portfolio through a
sentiment-aware Long Short Term Memory (LSTM) recurrent neural network for
price prediction and a mean-variance strategy. Our sentiment portfolio
strategies achieved on average a significant increase in revenue above the
non-sentiment aware models. However, the results show that our strategy does
not outperform traditional portfolio allocation strategies from a stability
perspective. We argue that an improved fusion of sentiment prediction with a
combination of price prediction and portfolio optimization leads to an enhanced
portfolio selection strategy.
