A Proposed Hybrid model for Sentiment Classification using CovNet-DualLSTM Techniques

  • Roop Ranjan
    a:1:{s:5:"en_US";s:31:"Madan Mohan Malaviya University";} roop.ranjan[at]gmail.com
  • AK Daniel
    Madan Mohan Malaviya University


The fast growth of Internet and social media has resulted in a significant quantity of texts based review that is posted on the platforms like social media. In the age of social media, analyzing the emotional context of comments using machine learning technology helps in understanding of QoS for any product or service. Analysis and classification of user’s review helps in improving the QoS (Quality of Services). Machine Learning techniques have evolved as a great tool for performing sentiment analysis of user’s. In contrast to traditional classification models. Bidirectional Long Short-Term Memory (BiLSTM) has obtained substantial outcomes and Convolution Neural Network (CNN) has shown promising outcomes in sentiment classification. CNN can successfully retrieve local information by utilizing convolutions and pooling layers. BiLSTM employs dual LSTM orientations for increasing the background knowledge accessible to deep learning based models. The hybrid model proposed here is to utilize the advantages of these two deep learning based models. Tweets of users for reviews of Indian Railway Services have been used as data source for analysis and classification. Keras Embedding technique is used as input source to the proposed hybrid model. The proposed model receives inputs and generates features with lower dimensions which generate a classification result. The performance of proposed hybrid model was   compared using Keras and Word2Vec and observed effective improvement in the response of the proposed model with an accuracy of 95.19%.
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Ranjan, R., & Daniel, A. K. (2022). A Proposed Hybrid model for Sentiment Classification using CovNet-DualLSTM Techniques. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(4), 401–418. https://doi.org/10.14201/ADCAIJ202110401418


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