The usual goal of sentiment analysis is to provide numeric measures of positive or negative valence for brands, products, and commodities, which can be aggregated over time or geographical regions to analyze patterns and trends. Dr. Shlomo Argamon discusses some new methods he and his team are developing which extend this paradigm in two ways. First, their systems analyze more aspects of each individual sentiment expression, including different types of attitude ("unwieldy" vs. "unreliable"), comparisons ("X is better than Y"), evaluative trends ("X is improving"), and modality ("possibly" vs. "likely" vs. "definitely"). Secondly, they are combining sentiment analysis with their methods for automated authorship profiling, which label texts with author characteristics such as gender, age, native language, education level, and so forth. When this is done, a new type of analysis emerges: data mining can be used to find "sentimental market segments", discovering, for example, that opinion is trending upwards for males aged 20-30, but downwards 30-50 year-olds who did not attend college. He presents some of their research results and discuss the implications for future applications and developments in sentiment analytics.