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Sentiment Analysis by Using Social Media Post
Subject area: Science,Engineering and Technology · Area of research: data science
Abstract
The Internet and online social networks have increased connection between people. Such information are often spread to too many of us in seconds. During this survey the results of variety of machine classi?ers like Support vector machine, K nearest Neighbor and Decision tree are used. Aim of classification of text concerning psychological state of user through comments on twitter also as Facebook. These algorithms distinguish between the more worrying content, like sad, happiness, anxiety, anger etc. Feeling examination might be a preparing to sort the look of the person who might be tweets are often named positive, negative or impartial. For case, the tweet "motion picture impressive" could also be a positive substance and therefore the tweet "motion pictures might be most noticeably awful" might be a negative substance. During this research they firstly explained about factors of depression. There are several signs and symptoms from which we will identify depression level of social media user
Keywords
classification, KNN, SVM, Social media post, sentiment analysis, facebook, twitter
How to cite this paper
@article{1702849,
author = {Ranjana Sargar, Reshma Gulwani, Dr. Vivek Kumar Singh},
title = {Sentiment Analysis by Using Social Media Post},
journal = {Iconic Research And Engineering Journals},
year = {2021},
volume = {5},
number = {1},
pages = {269-275},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1702849.pdf},
abstract = {The Internet and online social networks have increased connection between people. Such information are often spread to too many of us in seconds. During this survey the results of variety of machine classi?ers like Support vector machine, K nearest Neighbor and Decision tree are used. Aim of classification of text concerning psychological state of user through comments on twitter also as Facebook. These algorithms distinguish between the more worrying content, like sad, happiness, anxiety, anger etc. Feeling examination might be a preparing to sort the look of the person who might be tweets are often named positive, negative or impartial. For case, the tweet "motion picture impressive" could also be a positive substance and therefore the tweet "motion pictures might be most noticeably awful" might be a negative substance. During this research they firstly explained about factors of depression. There are several signs and symptoms from which we will identify depression level of social media user},
keywords = {classification, KNN, SVM, Social media post, sentiment analysis, facebook, twitter},
month = {July},
}