التفاصيل البيبلوغرافية
| العنوان: |
Neighbour adjusted dispersive flies optimization based deep hybrid sentiment analysis framework. |
| المؤلفون: |
Dey, Ranit Kumar, Das, Asit Kumar |
| المصدر: |
Multimedia Tools & Applications; Jul2024, Vol. 83 Issue 24, p64393-64416, 24p |
| مصطلحات موضوعية: |
ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, NATURAL language processing, LONG short-term memory, DEEP learning |
| مستخلص: |
A very crucial branch of Natural Language Processing is Sentiment Analysis, which seeks to elicit feelings in the public from feedback provided by users. This study proposes a Hybridized Deep Neural Network-based framework for the sentiment analysis, where we have modified Dispersive Flies Optimization by adjusting its neighbor counterpart and applied that Neighbour Adjusted Dispersive Flies optimization for optimizing feature space with the aid of sentiment information extracted using our specially developed SentiWordNet lexicon-linked fitness function, after preliminary processing of data. This modification helps to avoid the local optimal solution and supports the optimization process to approach the global optimal solution in more effective way. Next, to handle the textual features efficiently through Deep Learning approaches, we use pre-trained embedding technique to represent them mathematically. The Hybridized Deep Neural Network, which is made up of a Convolutional Neural Network and Long Short Term Memory, is then given the embedded features. In order to store locally implanted information, Convolutional Neural Networks construct hierarchical representations, while Long Short Term Memory attempts to recollect pertinent prior data for opinion categorization. This hybridization helps to take advantage of both the component networks. The deep neural network system ultimately delivers the desired sentiment category. To demonstrate its effectiveness, the suggested hybrid methodology is reckoned and contrasted with numerous cutting-edge methodologies utilizing a variety of performance indicators. Our proposed framework gives the best performance compared to the baselines with an accuracy of 89.0%, 81.9%, 67.9%, 64.6%, 83.2%, 79.8% and 91.3% for Amazon, ETSY, Big Basket, Facebook, Finance, Twitter and Wine dataset respectively. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |