Name
Abstract | ||
|---|---|---|
Aspect base sentiment analysis is a very popular concept in the machine learning era which is under the research domain still at the movement. This research mainly consist of the way of exploring the sentiment analysis based on the trained data set to provide the positive, negative and neutral reviews for different products in the marketing world. Most of the existing approaches for opinion mining are based on word level analysis of texts and are able to detect only explicitly expressed opinions. In aspect-based sentiment analysis (ABSA) the aim is to identify the aspects of entities and the sentiment expressed for each aspect. The ultimate goal is to be able to generate summaries listing all the aspects and their overall polarity.For this research mainly natural language and machine learning techniques are used. To train the application for the given data sets SVM (support vector machine) and ME (Maximum Entropy) classification algorithms have been used. Differentiation of the performance of the each algorithm will be analyzed through this research using the proven technologies available in the world like "Re call", "F-Measure" and Accuracy. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CIT.2016.107 | 2016 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY (CIT) |
Keywords | Field | DocType |
Aspect-based sentiment analysis (ABSA), SVM (support vector machine), ME (Maximum Entropy) | Data set,Sentiment analysis,Computer science,Support vector machine,Supervised learning,Feature extraction,Natural language,Artificial intelligence,Principle of maximum entropy,Statistical classification,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nipuna Upeka Pannala | 1 | 1 | 0.34 |
| Chamira Priyamanthi Nawarathna | 2 | 1 | 0.34 |
| J. T. K. Jayakody | 3 | 1 | 0.34 |
| Lakmal Rupasinghe | 4 | 2 | 1.71 |
| Kesavan Krishnadeva | 5 | 1 | 1.02 |