In this handbook, our aim has been to compile a comprehensive collection of relevant research results, which cover the financial applications of sentiment classification in general, and sentiment classification in particular. This is a emerging and evolving topic area that has been impacted by (i) growth in social media, (ii) online information sources, (iii) evolution of data sciences, (iv) continued developments in machine learning and artificial intelligence and (v) maturing of financial technologies (fintech), which exploit speed of communications and computations. Whereas early applications of sentiment analysis have been in the domain of equities, the recent developments have covered other asset classes, specifically, fixed income, foreign exchange, energy products and commodities. In all these domains we have focused on three major application areas which are automated trading, fund rebalancing and risk qualification and control.