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@article{164345,
        author = {Harsh Kumar Saha and Priyanka Dubey and Vibhor Srivastava},
        title = {Enhancing Natural Language Understanding: A Comprehensive Study of Convolutional Neural Networks for Sentence Modelling in Sentiment Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {1451-1457},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164345},
        abstract = {This research presents a novel technique to sentiment analysis that use Convolutional Neural Networks (CNN) for semantic sentence modelling in the domain of Natural Language Processing (NLP). The approach improves Sentiment Analysis Systems by classifying sentiments as Positive, Negative, or Neutral, with applications ranging from Opinion Mining to Discourse Analysis. The study investigates a range of techniques, including vocabulary-based, rule-based, and deep learning paradigms, highlighting the need for advanced tools to grasp attitudes across languages. The approach defines dataset selection, data pre-processing, and classification techniques, and the experimental setting explains model training and assessment. The overall findings highlight the usefulness of language-specific methodologies in sentiment analysis, guiding future research areas. Overall, this study advances sentiment analysis by elucidating human emotions in multilingual environments.},
        keywords = {BERT (Bidirectional Encoder Representation from Transformers), Convolutional Neural Network (CNN), Corpus, Deep Belief Network (DBN), Hyperparameter, Lemmatization, Lexicons, Natural Language Processing, Tokenization , Word Sense Disambiguation (WSD),.},
        month = {},
        }