A semantic framework for sentiment analysis
Author(s):
Piyusha Balasheb Kale
Keywords:
Semantic framework, Sentiment analysis, BERT, Natural language processing, Deep learning, Contextual information.
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.
Article Details
Unique Paper ID: 160702

Publication Volume & Issue: Volume 10, Issue 1

Page(s): 1041 - 1044
Article Preview & Download


Share This Article

Conference Alert

NCSST-2023

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2023

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2023

Go To Issue



Call For Paper

Volume 10 Issue 1

Last Date for paper submitting for March Issue is 25 June 2023

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews