Name
Abstract | ||
|---|---|---|
In this paper, we attempt to classify tweets into root categories of the Amazon browse node hierarchy using a set of tweets with browse node ID labels, a much larger set of tweets without labels, and a set of Amazon reviews. Examining twitter data presents unique challenges in that the samples are short (under 140 characters) and often contain misspellings or abbreviations that are trivial for a human to decipher but difficult for a computer to parse. A variety of query and document expansion techniques are implemented in an effort to improve information retrieval to modest success. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Data mining,World Wide Web,Social media,DECIPHER,Computer science,Amazon rainforest,Artificial intelligence,Parsing,Hierarchy,Machine learning,E-commerce |
DocType | Volume | Citations |
Journal | abs/1511.08299 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| matthew long | 1 | 0 | 0.34 |
| Aditya Jami | 2 | 17 | 1.32 |
| Ashutosh Saxena | 3 | 4575 | 227.88 |