| N10-1120 | is different from traditional | topic-based text classification | . Topic-based text classification |
| W04-3239 | word-based decision stumps for | topic-based text classification | . To classify trees , we here |
| J11-3005 | selection techniques are helpful for | topic-based text classification | , but they can not select good |
| P09-1079 | document-level sentiment analysis . Unlike | topic-based text classification | , where a high accuracy can be |
| N10-1120 | 2002 ) , which is widely used in | topic-based text classification | . In the approach , a subjective |
| N10-1120 | topic-based text classification . | Topic-based text classification | is generally a linearly separable |
| P09-2041 | We take our starting point from | topic-based text classification | ( Dumais et al. , 1998 ; Joachims |
| P13-2093 | has achieved great successes in | topic-based text classification | , it disrupts word order , breaks |
| W10-2918 | . Compared to the traditional | topic-based text classification | , sentiment classification is |
| P09-1079 | more difficult task . One reason | topic-based text classification | is easier than polarity classification |
| J15-2004 | classification is smaller than that in | topic-based text classification | ( Pang and Lee 2008 ) . Pang |
| J15-2004 | more difficult than traditional | topic-based text classification | , despite the fact that the number |
