Improving opinion retrieval in social media by combining features-based coreferencing and memory-based learning

Social networks messaging typically contains a lot of implicit linguistic information partially due to restrictions on a message's length (i.e., few named entities, short sentences, no discourse structure, etc.). This may significantly impact several applications including opinion mining, sentiment analysis, etc., as data collection tasks such as opinion retrieval tasks will fail to obtain all the relevant messages whenever the target topic, objects, or features are not explicit within the texts. In order to address these issues, in this paper a novel adaptive approach for opinion retrieval is proposed. It combines natural-language co-referencing techniques, features-based linguistic preprocessing and memory-based learning to resolving implicit co-referencing within informal opinion texts by using underlying hierarchies of thread messages. Experiments were conducted to assess the ability of the model to improve opinion retrieval by resolving implicit entities and features, showing the promise of our opinion retrieval approach when compared to state-of-the-art methods using text data from social networks.