Emotion mining from text for actionable recommendations detailed survey

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 feeling which in turn can be utilised in specific applications such as: marketing, sale predictions, political surveys, health care, student-faculty culture, e-learning platforms, and social networks. This process of identifying and extracting information about the attitude of a speaker or writer about a topic, polarity, or emotion in a document is called sentiment analysis. There are variety of sources for extracting sentiment such as speech, music, facial expression. Due to the rich source of information available in the form of text data, this paper focuses on sentiment analysis and emotion mining from text, as well as discovering actionable patterns. The actionable patterns may suggest ways to alter the user's sentiment or emotion to a more positive or desirable state.