TY - GEN
T1 - Parallel Text Classification Using Sentiment
AU - Halingali, Aditya Basavaraj
AU - Ruchitha, S. R.
AU - Gopalakrishna Kini, N.
AU - Jyothi Upadhya, K.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - With the exponential growth of user-generated content on platforms like YouTube, efficient sentiment analysis methods are crucial for real-time insights into audience engagement and feedback. This paper uses the Naïve Bayes classifier combined with Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) frameworks to analyse sentiment of YouTube comments in parallel. By distributing sentiment computation tasks across multiple processors and leveraging GPU capabilities, this method significantly reduces analysis time for large-scale datasets. The results show that while both MPI and CUDA provide significant speedup over sequential execution, CUDA outperforms MPI in terms of speedup metrics, offering superior performance for large-scale data. This demonstrates the effectiveness of parallel approach, highlighting substantial improvements in processing speed, thus offering a scalable solution for high-volume data analysis.
AB - With the exponential growth of user-generated content on platforms like YouTube, efficient sentiment analysis methods are crucial for real-time insights into audience engagement and feedback. This paper uses the Naïve Bayes classifier combined with Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) frameworks to analyse sentiment of YouTube comments in parallel. By distributing sentiment computation tasks across multiple processors and leveraging GPU capabilities, this method significantly reduces analysis time for large-scale datasets. The results show that while both MPI and CUDA provide significant speedup over sequential execution, CUDA outperforms MPI in terms of speedup metrics, offering superior performance for large-scale data. This demonstrates the effectiveness of parallel approach, highlighting substantial improvements in processing speed, thus offering a scalable solution for high-volume data analysis.
UR - https://www.scopus.com/pages/publications/105023313318
UR - https://www.scopus.com/pages/publications/105023313318#tab=citedBy
U2 - 10.1007/978-981-96-9203-3_29
DO - 10.1007/978-981-96-9203-3_29
M3 - Conference contribution
AN - SCOPUS:105023313318
SN - 9789819692026
T3 - Lecture Notes in Electrical Engineering
SP - 353
EP - 362
BT - Recent Trends in Artificial Intelligence and Data Sciences - Select Proceedings of the 15th International Conference, CONFLUENCE 2025
A2 - Kumar, Sumit
A2 - Aggarwal, Garima
A2 - Unhelkar, Bhuvan
A2 - Pal, Raju
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th International Conference on Recent Trends in Artificial Intelligence and Data Sciences, CONFLUENCE 2025
Y2 - 16 January 2025 through 17 January 2025
ER -