Comparative Study of Leveraging Big Data Processing Techniques for Sentiment Analysis


Wong, Chris Ern Zer and Ong, Lee Yeng and Leow, Meng Chew (2023) Comparative Study of Leveraging Big Data Processing Techniques for Sentiment Analysis. In: 2023 10th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 20-21 September 2023, Palembang, Indonesia.

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Sentiment analysis, an essential task in natural language processing, plays a pivotal role in understanding sentiment and opinions expressed in textual data. However, with the exponential growth of social media and online platforms, the sheer volume of textual data presents challenges for efficient processing. Traditional approaches struggle to cope with the increased data size, necessitating the adoption of big data processing techniques. This study presents a comparative performance analysis of sentiment analysis, evaluating the utilization of a big data processing framework. The study compares three machine learning algorithms for sentiment analysis with and without the implementation of big data processing techniques, focusing on model training efficiency. Additionally, two textual feature extraction techniques are examined to assess their impact on the results. Evaluation of the models' performance is based on the average execution time for training. The study's findings indicate that SparkML's Random Forest significantly outperforms the traditional sci-kit learn's Random Forest in terms of training time.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Sentiment analysis
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 02 Jan 2024 07:53
Last Modified: 02 Jan 2024 07:53


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