Name
Title | ||
|---|---|---|
Consumer insight mining: Aspect based Twitter opinion mining of mobile phone reviews. |
Abstract | ||
|---|---|---|
•Creation of a Twitter sentiment analysis framework that is capable of analysing tweets about mobile phones on a feature level using spelling correction, abbreviation sentiment identification, emoji and emoticon detection.•Usage of lexicon based approaches to label training data in order to eliminate manual classification.•Exhibit the importance of techniques such as spelling correction and abbreviation sentiment identification when analysing social media data for sentiments.•Demonstrate the value of emoji detection which shows the need to improve the social media based sentiment analysis tools by adding the feature of emoji detection. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.asoc.2017.07.056 | Applied Soft Computing |
Keywords | Field | DocType |
Emoji,Ontology,Sentiment analysis,Social media,Twitter,SVM | Ontology,Data mining,Emoji,Emoticon,Computer science,Natural language processing,Artificial intelligence,Mobile phone,Classifier (linguistics),Sentiment analysis,Supervised learning,Lexicon,Machine learning | Journal |
Volume | ISSN | Citations |
68 | 1568-4946 | 2 |
PageRank | References | Authors |
0.38 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rathan M. | 1 | 2 | 0.38 |
| Vishwanath R. Hulipalled | 2 | 2 | 0.38 |
| K. R. Venugopal | 3 | 267 | 48.80 |
| L. M. Patnaik | 4 | 165 | 15.46 |