The goal of this project is to use machine learning and NLP to generate a helpful aspect-based summary from the raw text of reviews about a particular restaurant or product. An aspect-based summary of a set of reviews about, say, a pizza place might look as follows:
- Pizza: 5/5
- "…I loved the pizza here!" - Joe P.
- Wine: 3/5
- "The wine here is excellent…" - Jen A.
- Service: 2/5
- "…service was slow here…" - Tom B.
- Ambiance: 3/5
- "I really enjoyed the atmosphere…" - Sam K.
where "Pizza", "Wine", "Service", and "Ambiance" are the aspects of the restaurant which are most commonly mentioned by reviewers, and the scores (e.g. 3/5) reflect reviewers' overall attitudes toward the corresponding aspect. A summary of this form allows consumers to quickly understand a large body of reviews about a product or service and thereby make an informed decision about what or where to buy.
See ./docs/proposal.md for more details on this project.
References:
The problem of aspect-based opinion mining has been addressed in academic literature. See especially:
- Blair-Goldensohn et al.'s "Building a Sentiment Summarizer for Local Service Reviews" (2008)
- Bing Liu's Sentiment Analysis and Opinion Mining (2012)
- Hu & Liu's Mining and Summarizing Customer Reviews (2004)