Harnessing Twitter for Answering Opinion List Queries

Abstract

Opinion list (OL) queries like “valentines day gift ideas” and “best anniversary messages for your parents” are quite popular on web search engines. Users expect instant answers comprising of a list of relevant items (OL) for such a query. Surprisingly, current search engines do not provide any crisp instant answers for queries in this critical query segment. To the best of our knowledge, we present the first system that tackles such queries. Although such social factors are heavily discussed on online social networks like Twitter, extracting such lists from tweets is quite challenging. The challenges lie in discovering such lists from tweets, rank the discovered list items as well as handle lists with very low cardinality (tail OLs). We present an end-to-end system that: 1) identifies these “OLs” from a large number of Twitter hashtags using a classifier trained using novel task-specific features; 2) extracts suitable list answers from relevant tweets using carefully designed regex patterns; 3) uses the learning to rank framework to present a ranked list of these items; and 4) handles tail lists using a novel algorithm to borrow list items from similar lists. Crowd-sourced evaluation shows that the proposed system can extract OLs with a good accuracy.