In the era of Web 2.0, people express their opinion, feelings and thoughts about topics including political and cultural events, natural disasters, products and services, through mediums such as blogs, forums, and micro-blogs, like Twitter. Also, large amount of text is generated through e-mail which contains the writer's feeling or opinion; for instance, customer care service e-mail. The texts generated through such platforms are a rich source of data, which can be mined in order to gain useful information about user opinion or feelings. Sentiment Analysis identifies and extracts information about the attitude of a speaker or writer on a subject, topic, polarity, or emotion in a document. Sentiments can be extracted from sources such as speech, music, and facial expression. Due to rich source of information available in the form of text data, we focus on sentiment analysis and emotion mining from text. We further discover Actionable Patterns from these sentiments, which suggest ways to alter the user's emotion to a more positive or desirable state. Little work has been done on extracting Actionable Recommendations from sentiments. We contribute to the solution of this challenging problem by applying machine learning methods such as decision forest, and support vector machines for emotion classification, and Action Rules Mining for Emotion altering recommendations. We experiment with live streaming Twitter Data, Student Evaluations data, Business Net Promotor Score data. Results show high accuracy for Emotion Detection, and successful discovery of Actionable Recommendations for more positive user sentiment. Applications of this work include: marketing, sale predictions, political surveys, health care, student-faculty culture, e-learning platforms, and social networks.
