Text mining applications in the investment process involves a complex interaction between computational linguistics, natural language processing (NLP) and the know-how of the financial aspects. Given the progress in big data and multimodal data fusion, this state-of-the-art survey provides a timely consolidation of this ever evolving topic, together with new perspectives on the acquisition, input, variable relevance, feature extraction, fusion, and decision making based on a conjoint treatment of text and standard financial variables. Such an insight is then used as a basis to introduce an overarching framework for text-based big data in the investment process. The proposed approach is both novel and flexible, making it possible to be seamlessly employed across a variety of investable assets, including stocks, credit instruments, rates, FX and market indices. Another unique aspect is its modularity, whereby both emerging techniques in signal processing and machine learning, as well as traditional econometric techniques, are readily incorporated and combined towards the informed decision. Another virtue of the proposed concept is its ability to identify the semantic nature (context) of the source, even for general text-based sources (financial reports, social media, market news) while at the same time maintaining investors intuition, as news do affect asset prices and market moves. An example of a recent stock market performance during a company takeover process demonstrates the advantages of the proposed framework.