Suggestion Mining from Opinionated Text

In addition to the positive and negative sentiments expressed by speakers, opinions on the web also convey suggestions. Such text comprise of advice, recommendations and tips on a variety of points of interest. We propose that suggestions can be extracted from the available opinionated text and put to several use cases. The problem has been identified only recently as a viable task, and there is a lot of scope for research in the direction of problem definition, datasets, and methods. From an abstract view, standard algorithms for tasks like sentence classification and keyphrase extraction appear to be usable for suggestion mining. However, initial experiments reveal that there is a need for new methods, or variations in the existing ones for addressing the problem specific challenges. We present a research proposal which divides the problem into three main research questions; we walk through them, presenting our analysis, results, and future directions.