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@article{160702,
        author = {Piyusha Balasheb Kale},
        title = {A semantic framework for sentiment analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {1},
        pages = {1041-1044},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=160702},
        abstract = {Sentiment analysis, the process of determining the sentiment or emotional tone conveyed in textual data, plays a crucial role in various applications such as social media monitoring, customer feedback analysis, and market research. Recent advancements in natural language processing (NLP) and deep learning have led to the development of powerful models like BERT (Bidirectional Encoder Representations from Transformers) that have revolutionized the field of sentiment analysis. a semantic framework for sentiment analysis using BERT, aiming to enhance the accuracy and interpretability of sentiment classification tasks. The framework leverages BERT's ability to capture contextual information by pre-training on large-scale unlabeled text data and fine-tuning on sentiment-labeled datasets.},
        keywords = {Semantic framework, Sentiment analysis, BERT, Natural language processing,
Deep learning, Contextual information.
},
        month = {},
        }