Thispaperpresentsacomparativestudyontheperformanceofbinary- and multi-class Deep Neural Network classification models that have been trained with the optimized hyperparameters. Four sequential models are developed using Tensorflow and Keras to perform sentiment classification of product reviews posted on Amazon, IMDb, Twitter and Yelp. Preprocessing is carried out to cleanse text reviews and to extract informative features. 5-fold cross evaluation results demonstrate that the final multiclass model perform with an accuracy of 74.7% and 72.4%, whereas the binary model achieves of 80.0% and 69.5% indi- cating better performance. The conclusion of this study is that an optimized binary model architecture can be used to train multiclass classification models and save significant amounts of computing time.
|Title of host publication||Knowledge Science, Engineering and Management - 14th International Conference, KSEM 2021, Proceedings|
|Subtitle of host publication||KSEM 2021|
|Number of pages||12|
|Publication status||Published online - 7 Aug 2021|
|Event||The 14th International Conference on Knowledge Science, Engineering and Management (KSEM 2021) - Tokyo, Japan|
Duration: 14 Aug 2021 → 16 Aug 2021
|Name||Lecture Notes in Computer Science|
|Conference||The 14th International Conference on Knowledge Science, Engineering and Management (KSEM 2021)|
|Abbreviated title||KSEM 2021|
|Period||14/08/21 → 16/08/21|
Bibliographical notePublisher Copyright:
© 2021, Springer Nature Switzerland AG.
- Sentiment analysis
- Deep neural networks
- Word embedding