2
Universiti Utara Malaysia, Sintok, Kedah, Malaysia
3
Universiti Putra Malaysia
4
Cluster of Education and Social Sciences, Open University Malaysia
Abstract
In this work using machine learning techniques for the sentiment analysis was observed. Sentiment analysis using unsupervised supervised and semi-supervised techniques are analyzed. Sentiment analysis has different types but using it with machine learning algorithms improves the overall results of the problem. The overall methods to eliminate the odd posts have analyzed the importance of the odd posts in the current era with an exponential increase of the data is explained. The social media applications are the major source of this huge data, and to remain such platforms secure to overcome odd posts issue, the requirement was to construct an approach to eliminate the odd posts. Overall, RNN produces high accuracy results, on the tweets data set in eliminating the odd posts from the tweets.
Rahmat, R., Hamid, H., Razale, M., & Nordin, M. (2021). Machine Learning In Smart Technology Warehouses. Int. J. of Aquatic Science, 12(1), 103-113.
MLA
Robaisya Rahmat; Hashibah Hamid; MohdHafizuddin Razale; MohdNorazmi Nordin. "Machine Learning In Smart Technology Warehouses". Int. J. of Aquatic Science, 12, 1, 2021, 103-113.
HARVARD
Rahmat, R., Hamid, H., Razale, M., Nordin, M. (2021). 'Machine Learning In Smart Technology Warehouses', Int. J. of Aquatic Science, 12(1), pp. 103-113.
VANCOUVER
Rahmat, R., Hamid, H., Razale, M., Nordin, M. Machine Learning In Smart Technology Warehouses. Int. J. of Aquatic Science, 2021; 12(1): 103-113.
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