An efficient sentiment analysis methodology based on long short-term memory networks
Sentiment analysis is the process of determining the sentiment polarity (positivity, neutrality or negativity) of the text. As online markets have become more popular over the past decades, online retailers and merchants are asking their buyers to share their opinions about the products they have pu...
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Veröffentlicht in: | Complex & Intelligent Systems 2021-10, Vol.7 (5), p.2485-2501 |
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description | Sentiment analysis is the process of determining the sentiment polarity (positivity, neutrality or negativity) of the text. As online markets have become more popular over the past decades, online retailers and merchants are asking their buyers to share their opinions about the products they have purchased. As a result, millions of reviews are generated daily, making it difficult to make a good decision about whether a consumer should buy a product. Analyzing these enormous concepts is difficult and time-consuming for product manufacturers. Deep learning is the current research interest in Natural language processing. In the proposed model, Skip-gram architecture is used for better feature extraction of semantic and contextual information of words. LSTM (long short-term memory) is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are optimized by the adaptive particle Swarm Optimization algorithm. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models in different metrics. |
doi_str_mv | 10.1007/s40747-021-00436-4 |
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Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. 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subjects | Algorithms Complexity Computational Intelligence Computational linguistics Data mining Data Structures and Information Theory Decision analysis Engineering Feature extraction Language processing Machine learning Mathematical optimization Model accuracy Natural language interfaces Natural language processing Original Article Particle swarm optimization Sentiment analysis Short term Social networks |
title | An efficient sentiment analysis methodology based on long short-term memory networks |
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