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<p>Due to the proliferation of Web 2.0 tools, users today produce massive volumes of data in a very dynamic manner. In this sense, sentiment analysis seemed to be a key tool for automatically extracting the inside knowledge from data provided by users. It is vital to evaluate and employ the data given by the user individual to utilize it to the most for understanding the sentiment of individuals from a given text in an automated manner, considering the speed at which data is being generated by internet users across a variety of platforms. The manual extraction of features procedure, which is a key component of feature-driven changes, is used by conventional approaches such as the traditional machine learning and surface techniques. Techniques based on deep learning are currently being tested for a variety of sentiment analysis applications and have produced the most effective outcomes. As a result, an intelligent ensemble network for analyzing the sentiment in an automated manner is designed. The required data is obtained from the necessary sources for analyzing the sentiments in the initial stages. The input text data is provided in the process of text cleaning. Next, the cleaned text passed to an Adaptive Bidirectional Encoder Representations from Transformers (ABERT) based Word Embedding framework to produce the feature vector. This ABERT network makes use of the Improved Ebola Optimization Search Algorithm (IEOSA) to determine the optimal parameters. The feature vector extracted from the ABERT is finally given to the suggested Ensemble Deep Learning with Attention Network (EDLAN), which consists of the Deep Temporal Convolution Network (DTCN), Residual Long Short-Term Memory (ResLSTM), Deep Markov Random Field (DMRF), Deep Belief Network (DBN), and Gated Recurrent Unit (GRU) to analysis the sentiments in the given feature vector. The analyzed sentiments from these models are then considered for high-ranking techniques to obtain the final analyzed sentiment results. The outcome of the implemented sentiment analysis framework is examined, and the outcomes are compared to more conventional methods.</p>

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Adaptive BERT-assisted feature vector representation and ensemble deep learning approach for sentiment analysis with heuristic improvement

  • 期刊论文

  • 发表日期：

    2025年10月21日

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  • Anil kumar Dubey,
  • Mala Saraswat

摘要

Due to the proliferation of Web 2.0 tools, users today produce massive volumes of data in a very dynamic manner. In this sense, sentiment analysis seemed to be a key tool for automatically extracting the inside knowledge from data provided by users. It is vital to evaluate and employ the data given by the user individual to utilize it to the most for understanding the sentiment of individuals from a given text in an automated manner, considering the speed at which data is being generated by internet users across a variety of platforms. The manual extraction of features procedure, which is a key component of feature-driven changes, is used by conventional approaches such as the traditional machine learning and surface techniques. Techniques based on deep learning are currently being tested for a variety of sentiment analysis applications and have produced the most effective outcomes. As a result, an intelligent ensemble network for analyzing the sentiment in an automated manner is designed. The required data is obtained from the necessary sources for analyzing the sentiments in the initial stages. The input text data is provided in the process of text cleaning. Next, the cleaned text passed to an Adaptive Bidirectional Encoder Representations from Transformers (ABERT) based Word Embedding framework to produce the feature vector. This ABERT network makes use of the Improved Ebola Optimization Search Algorithm (IEOSA) to determine the optimal parameters. The feature vector extracted from the ABERT is finally given to the suggested Ensemble Deep Learning with Attention Network (EDLAN), which consists of the Deep Temporal Convolution Network (DTCN), Residual Long Short-Term Memory (ResLSTM), Deep Markov Random Field (DMRF), Deep Belief Network (DBN), and Gated Recurrent Unit (GRU) to analysis the sentiments in the given feature vector. The analyzed sentiments from these models are then considered for high-ranking techniques to obtain the final analyzed sentiment results. The outcome of the implemented sentiment analysis framework is examined, and the outcomes are compared to more conventional methods.