Social Media Mining Using Machine Learning Techniques as a Survey

DOI

10.2991/978-94-6463-136-4_77How to use a DOI?

Keywords

Social Media; Machine learning; Support Vector Machine; Clustering; Map Reduce; Deep learning

Abstract

In today’s world an online existence and social media users utilize various social media platforms to express or comments their observations and opinions. The role of social media platforms are predicting Government Initiatives, Election results, product Analysis, business analysis, movie popularity, sports outcomes and stock market analysis. This review paper proposed the opinions are expressed through different social media platforms can be used for retrieving or extracting the real time predictions on several trends. As per the sentiment identification outcome find the features in the form of Positive (+ve), Negative (−ve) and Neutral (=). In this proposed research methodology, here collect user’s reviews on particular trends, then preprocessed it, creation of the features and selecting for data classification using different machine leering classifiers and predict the result. For better performance, used advanced preprocessing techniques will be applied to cleaning the data. For Sentiment Classification will be used machine learning algorithms or techniques like (SVM) Support Vector machine, (ME) Maximum Entropy, (NB) Naïve Bayes and (DT) Decision tree. As per existing techniques, It is very difficult to mine the correct predictions from social media. Therefore, the prediction model will be designed for doing the prediction using real time data from Twitter. An opinion from text or comment posted on social media platforms by various categories of users is one of the critical and time consuming tasks in the field of opining mining and analysis. The importance of this proposed intelligent system for social media is to automatically providing polarity from unstructured data in the form of text in English language for effective decision making.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ashish A. Bhalerao
AU  - Bharat R. Naiknaware
AU  - Ramesh R. Manza
AU  - Vandana Bagal
AU  - Shobha K. Bawiskar
PY  - 2023
DA  - 2023/05/01
TI  - Social Media Mining Using Machine Learning Techniques as a Survey
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 874
EP  - 889
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_77
DO  - 10.2991/978-94-6463-136-4_77
ID  - Bhalerao2023
ER  -