Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-93659-8_22 | COMPLEX, INTELLIGENT, AND SOFTWARE INTENSIVE SYSTEMS |
Field | DocType | Volume |
Ontology,Information retrieval,Computer science,Sentiment analysis,Distributed computing | Conference | 772 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Monireh Alsadat Mirtalaie | 1 | 5 | 1.76 |
| Omar Khadeer Hussain | 2 | 406 | 56.97 |
| Elizabeth Chang | 3 | 1017 | 108.04 |
| Farookh Khadeer Hussain | 4 | 857 | 90.09 |