Enhancing Text Mining Using Deep Learning Models
| Autor: | Mukund Garg, Avinash C. Pandey, Sonali Rajput |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Synthetic intelligence Deep learning 02 engineering and technology Semi-supervised learning Space (commercial competition) Machine learning computer.software_genre Support vector machine 020901 industrial engineering & automation Text mining 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Word (computer architecture) Heap (data structure) |
| Zdroj: | IC3 |
| DOI: | 10.1109/ic3.2019.8844895 |
| Popis: | Text mining, a section of the synthetic intelligence, is gaining grounds nowadays in terms of the applications in business and analysis. Varied sectors and domains across industries understand the potential of text mining in gaining information, mining helpful data and in enhancing the choice creating method in terms of speed and potency. In today's world, where the web area is overflowing with data, text mining technology and solutions will persuade be the turning purpose. Most of the recent researches conducted during this space focused principally on advanced or the hybrid of deep neural networks so as to induce economical and higher results. Functioning on such serious models isn't solely time taking however conjointly needs the usage of heap of resources. Therefore, to get the comparable results only basic deep learning models have been used in order to minimize the model's complexity and computational cost. For the same, RNN and LSTM based models have been used and accuracy of proposed models have been enhanced by by varying the hyper parameters and using Glove word embeddings. Moreover, a labeled dataset named Sentiment5k has also been created using semi-supervised learning approach for evaluating the performance of RNN & LSTM based models. |
| Databáze: | OpenAIRE |
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