Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

Shobana, J.; Murali, M.

doi:10.3233/jifs-201644

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Their RecogNet-LSTM+CNN model with attention mechanism showed superior performance in aspect categorization and opinion classification. In 2021, Shobana and Murali [34] proposed an adaptive particle swarm optimization algorithm based on long short-term memory networks. They combined an opposition-based learning method with a particle swarm optimization algorithm to enhance weight parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews

Liu

Wang

2023

Applied Sciences

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With the rapid development of the Internet, agricultural products have entered e-commerce platforms, and agricultural product reviews have become an important reference for consumers when purchasing agricultural products. However, due to the characteristics of different lengths, rich context-sensitive information, and multi-level information in the sentences of agricultural product reviews, the existing sentiment analysis methods cannot perform well enough to identify the sentiment tendency. To address this issue, we abstract the problem as a binary classification task to extract consumers’ sentiment orientation by proposing a new method. This method utilizes an attention mechanism to assign different weights to different key information in the sentence, thereby extracting abundant semantic information from the sentence. The design of the long short-term memory (LSTM) gate can effectively solve the problem of extracting long sequences and context-related information. The proposed model achieves superior results on two agricultural product datasets compared to other baseline models, providing guidance for merchants to improve agricultural product quality and enhance customer satisfaction.

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Section: Literature Reviewmentioning

confidence: 99%

AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews

Liu

Wang

2023

Applied Sciences

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“…Even though all attributes may be valuable in some circumstances, just a preferred number of attributes [9] are frequently used for identifying targets. In the KDD CUP 2015 dataset, an activity log is often used to collect several sorts of information regarding learners' behavior.…”

Section: Introductionmentioning

confidence: 99%

MOOC Dropout Prediction using FIAR-ANN Model based on Learner Behavioral Features

Nithya¹,

S.Umarani²

2022

IJACSA

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Massive Open Online Courses (MOOCs) are a transformative technology in digital learning that incorporates new techniques through video sessions, exams, activities, and conversations. Everyone leads a successful life in their professional and personal skills learning courses during COVID-19. The research concentrated on employing video interaction analysis to characterize crucial MOOC tasks, including predicting dropouts and student achievement. Our work consists of merely generating and picking the best characteristics based on the learner behavior for evaluating the dropout measure. To locate the frequent objects for feature creation, an association rule-FP growth approach is applied. The neural network is implemented using frequent itemset-3, which is used for feature selection. The evaluation metrics are calculated by using the Multilayer Perceptron (MLP) method. The metric values were then compared to the proposed model and some base supervised machine learning models namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB). The FIAR (Feature Importance Association Rule)-ANN(Artificial Neural Network) dropout prediction model was tested on the KDD Cup 2015 dataset and it had a high accuracy of over 92.42, which is approximately 18% better than the MLP-NN model. With the optimized parameters, we are solely focused on lowering dropout rates and increasing learner retention.

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Modified Extreme Learning Machine Algorithm with Deterministic Weight Modification for Investment Decisions based on Sentiment Analysis

Kalaiselvi,

David

2023

RACSC

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Background: A significant problem in economics is stock market prediction. Due to the noise and volatility, however, timely prediction is typically regarded as one of the most difficult challenges. A sentiment-based stock price prediction that takes investors' emotional trends into account to overcome these difficulties is essential. Objective: This study aims to enhance the ELM's generalization performance and prediction accuracy. Methods: This article presents a new sentiment analysis based-stock prediction method using a modified extreme learning machine (ELM) with deterministic weight modification (DWM) called S-DELM. First, investor sentiment is used in stock prediction, which can considerably increase the model's predictive power. Hence, a convolutional neural network (CNN) is used to classify the user comments. Second, DWM is applied to optimize the weights and biases of ELM. Results: The results of the experiments demonstrate that the S-DELM may not only increase prediction accuracy but also shorten prediction time, and investors' emotional tendencies are proven to help them achieve the expected results. Conclusion: The performance of S-DELM is compared with different variants of ELM and some conventional method.

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Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

Cited by 4 publications

References 27 publications

AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews

AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews

MOOC Dropout Prediction using FIAR-ANN Model based on Learner Behavioral Features

Modified Extreme Learning Machine Algorithm with Deterministic Weight Modification for Investment Decisions based on Sentiment Analysis

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