With the upsurge of Web 2.0, customers are able to share their opinions and feelings about products and services, politics, economic shifts, current events and any number of other topics on the Web. This information, if leveraged effectively, can provide rich and valuable insights, such as: input for vendors to create successful marketing strategies, understanding of areas of improvement in products and services and tracking political opinion.The problem with this information is that it is unorganised and unstructured, therefore, it is difficult to assess automatically and in bulk. Studies in the field of sentiment analysis aim to provide a solution to determining the polarities of, and gain an overview of, the wider public opinion behind certain topics in a large volume of textual data. This research provides a novel framework and a solid, practical implementation of the proposed framework for fine-grained sentiment analysis. The framework supports mixed-opinion text and multiword expressions when analysing the sentiments expressed and the aspects that those sentiments relate to.This research uses datasets across two domains in the customer reviews area (phone products and hotel services) to evaluate the proposed framework for its reliability and validity. A sizeable performance improvement was noted whereby the proposed methodology yielded a result of 91.3% accuracy in sentiment classification, as compared to the baseline (SentiWordNet), which had a result of 71.0%. In addition, an accuracy of 92.5% was observed for the aspect analysis automatically generated across the two domains tested.
|Date of Award||1 Aug 2017|
- The University of Manchester
|Supervisor||Ilias Petrounias (Supervisor) & Pedro Sampaio (Supervisor)|
- aspect analysis
- Sentiment analysis