.png){kind=icon}
.png){kind=icon}
Return
An Ensemble-Based Model for Sentiment Analysis of Persian Comments on Instagram Using Deep Learning Algorithms
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3473617.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
On a daily basis, an abundance of opinions, thousands or even millions of comments are generated by various individuals on social media. Collecting and evaluating these comments using traditional methods and algorithms is accompanied by less accuracy. Therefore, the development of a robust sentiment analysis system is essential for the accurate analysis of users' sentiments. Current methods have limited accuracy. Therefore, an idea to overcome this limitation is to get benefit of several classifiers together. Ensemble methods, through the combination of several different algorithms with diverse structures, can generate a new framework capable of better analyzing the sentiments. In the present study, an ensemble-based model is introduced to extract meaningful information from Persian comments on the Instagram social media platform. The model is proposed for the classification and prediction of users' behaviors or emotions across distinct categories. This hybrid model comprises three main phases. The first phase is pre-processing and word embedding. Word2Vec is used for this manner. The second phase consists of four proposed deep models, namely CNN, LSTM, CNN-LSTM, and LSTM-CNN which are used as classifiers. Finally, in the third phase, ensemble techniques like MLP and Voting ensemble are employed to aggregate the results derived from the previous phase. To evaluate the performance of the proposed ensemble-based model, the model is applied to the Insta.csv dataset, containing Persian comments on Instagram. Experimental results demonstrate that the proposed ensemble-based model, utilizing the Voting ensemble, outperforms other ensemble methods. In terms of accuracy, it achieves 72.337%, therefore, the Voting ensemble shows a 4.9% improvement over the MLP ensemble.
Keywords:
Ensemble learning
Long short term memory
Accuracy
Sentiment analysis
Feature extraction
Web sites
Multimedia communication
Analytical models
Convolutional neural networks
Social networking (online)
Deep learning
deep learning
ensemble learning
voting method
CNN
LSTM
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
View more organizations