Mining Product Experiences from User Generated Reviews: A Recommender Systems Perspective

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Title: Mining Product Experiences from User Generated Reviews: A Recommender Systems Perspective
Authors: Muhammad, Khalil
Lawlor, Aonghus
Rafter, Rachael
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/6456
Date: Sep-2014
Abstract: We have employed algorithms described in to mine opinions from TripAdvisor hotel reviews; we have experimented with different parameters to learn which provided more meaningful extractions. Secondly, we have considered opinion summarization and search similar to. We have implemented a retrieval strategy that accepts natural language queries based on opinions from reviews. Additionally, we have proposed various methods of summarizing opinions based on statistical metrics. Currently, we are experimenting with feature quality metrics. Our aim is to establish a relevance score that describes the usefulness of extracted opinions. We are also running recommendation experiments using different versions the extracted opinions.
Type of material: Conference Publication
Keywords: Recommender systems;Product descriptions
Language: en
Status of Item: Peer reviewed
Conference Details: 22nd International Conference on Case-Based Reasoning, Cork, Ireland, 29 September - 01 October 2014
Appears in Collections:Insight Research Collection

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