| 000 | 00881nam a22001697a 4500 | ||
|---|---|---|---|
| 999 |
_c209474 _d209474 |
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| 005 | 20220519125238.0 | ||
| 022 | _a2514-9288 | ||
| 050 | _ahttps://www.emerald.com/insight/publication/issn/2514-9288/vol/53/iss/1 | ||
| 245 | _aData Technologies and Applications | ||
| 260 | _aEmerald | ||
| 310 | _aQuarterly | ||
| 362 | _aVol 53, No. 1, 2019 | ||
| 520 | _a The fabulous results of convolution neural networks in image-related tasks attracted attention of text mining, sentiment analysis and other text analysis researchers. It is, however, difficult to find enough data for feeding such networks, optimize their parameters, and make the right design choices when constructing network architectures | ||
| 650 |
_2Sentiment analysis _aOpinion mining _vOpinion mining, |
||
| 856 | _uhttps://www.emerald.com/insight/publication/issn/2514-9288/vol/53/iss/1 | ||
| 942 | _cE-RESOURCE | ||