Machine Learning Prospects: Insights for Social Media Data  Mining and Analytics

Anu Sharma; Vivek Kumar

doi:10.55524/ijircst.2023.11.3.3

Authors

Anu Sharma Assistant Professor, Department of Computer Science & Engineering, Teerthanker Mahaveer University, Moradabad, India Author
Vivek Kumar Assistant Professor, Department of Computer Science & Engineering, Teerthanker Mahaveer University, Moradabad, India Author

DOI:

https://doi.org/10.55524/ijircst.2023.11.3.3

Keywords:

Data mining, Social media data, deep learning, Machine learning, Naïve Bayes, Maximum entropy

Abstract

Social network has increased surprising consideration in the most recent decade .Social network deals with enormous volume of composite as well as unstructured data and they are very hard to handle. Due to expanding dimensions and demand, one of the encouragingand interesting research field becomes social network. Data Mining affirms to get knowledge by discovery patterns among data. We have discussed social media mining and Social Media analytics. We have insights on the social media effect of our lives, some facts and reports from various sources. We have Integrated this growing research field of social networks with Machine Learning with one simple example of sentiment analysis of Twitter data using Machine Learning. We have also proposed the algorithms to improve the social media analytics results using Machine Learning. In this paper, we will exhibit how machine learning will utilizing for social networking systems like Twitter. In this procedure, a framework is proposed that will collect the tweets messages from the and we will inspect the item’s input to show the positive, negative, or nonpartisan tweets, for this this purpose we have proposed new machine learning algorithms Naive Bayes, maximum entropy to find these outputs. Our proposed Model will help neresearchers, companies, Industries, business community, practitioners, new integrated application designers, and the global community to solve the new research problem and may reducing design failure rate of 80% by large through social media mining and networks.

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