A fine-grained ontology-based sentiment aggregation approach
- Publisher:
- Springer
- Publication Type:
- Conference Proceeding
- Citation:
- Advances in Intelligent Systems and Computing, 2019, 772, pp. 252-262
- Issue Date:
- 2019-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| COR10253424.pdf | Published version | 717.96 kB |
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© 2019, Springer International Publishing AG, part of Springer Nature. Sentiment analysis techniques are widely used to capture the voice of customers about different products/services. Aspect or feature-based sentiment detection tools as one of the sentiment analyses’ types are developed to find the customers’ opinions about various features of a product. However, as a product may contain many features, presenting the final obtained results to the users is a challenge. Even though this issue is addressed in the literature by developing different sentiment aggregation methods, their results are mostly presented at the basic-level features of a product. This may cause in losing customers’ opinion about at minor sub-features. However, as the performance of a basic feature is dependent on those of its different sub-features, we propose an approach which aggregates the extracted results at a fine-grained level features using a product ontology tree. We interpret the polarity of each feature as a satisfaction score which can help managers in investigating the weaknesses of their products even at minor levels in a more informed way.
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