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Sentiment Analysis by Using Social Media Post

Ranjana Sargar Reshma Gulwani Dr. Vivek Kumar Singh

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

References

[1] Islam, Md Rafiqul & Kabir, Ashad & Ahmed, Ashir & Kamal, Abu & Wang, Hua & Ulhaq, Anwaar. (2018). Depression detection from social network data using machine learning techniques. Health Information Science and Systems. 6. 8. 10.1007/s13755-018-0046-0.

[2] Arvind and Deva Prasad “Text Recognizer and Document Reader in Various Languages and Accents” 2017I. S. Jacobs and C. P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271–350.

[3] M M Aldarwish and H.F.Ahmed,” Predicating Depression Levels Using Social Media Post,”2017, IEEE 13th International Symposium on Autonoums Decentralized System (ISADS), Bangkok 2017, pp.277-280

[4] Gamon, Michael & Choudhury, Munmun & Counts, Scott & Horvitz, Eric. (2013). Predicting Depression via Social Media. Association for the Advancement of Artificial Intelligence. Christo Troussa, Maria, Kurt Junshean Espinosa, Kevin liaguno,Jasme cargo sentiment analysis of Facebook statuses using naïve Bayes classifier for language learning 2013.

[5] Rupinder Kaur , Dr. Harman deep Singh , Dr. Gaurav Gupta, 2019, Sentimental Analysis on Facebook comments using Data Mining Technique, International Journal Of Engineering Research & Technology (IJERT) Volume 08, Issue 08 (August 2019).

[6] M.Ciric , A.Stanimirovic , N.Petrovic and L.Stoimeniv , “ Comparison of different algorithms for sentiment classification,”2013 11th International Conference on Telecommunication in Modern Satellite, cable and Broadcasting Services (TELSIKS),Nis,2013,pp.567570.doi:10.1109/TELSKS.2013.6704442

[7] A. M. Ramadhani and H. S. Goo, "Twitter sentiment analysis using deep learning methods," 2017 7th International Annual Engineering Seminar(InAES),Yogyakarta,2017,pp.14.doi:10.1109/INAES.2017.8068556

[8] Varsha Sahayak, Vijaya Shete, ApashabiPathan, “Sentiment Analysis on Twitter Data”, International Journal of Innovative Research in Advanced Engineering (IJIRAE), Issue 1, Volume 2, January2015,pp.178-183.

[9] Purva Mestry, Shruti Joshi, Sonal Mehta and Ashwini Save. Article: A Survey on Twitter Sentiment Analysis with Various Algorithms. IJCA Proceedings on National Conference on Role of Engineers in National Building NCRENB2016 (1):20-24, July2016. Pp.35-38.

How to cite this paper

Ranjana Sargar, Reshma Gulwani, Dr. Vivek Kumar Singh "Sentiment Analysis by Using Social Media Post" Iconic Research And Engineering Journals Volume 5 Issue 1 2021 Page 269-275
Ranjana Sargar, Reshma Gulwani, Dr. Vivek Kumar Singh "Sentiment Analysis by Using Social Media Post" Iconic Research And Engineering Journals, vol. 5, no. 1, Jul. 2021
Ranjana Sargar, Reshma Gulwani, Dr. Vivek Kumar Singh (2021). Sentiment Analysis by Using Social Media Post. Iconic Research And Engineering Journals, 5(1).
Ranjana Sargar, Reshma Gulwani, Dr. Vivek Kumar Singh "Sentiment Analysis by Using Social Media Post" Iconic Research And Engineering Journals, vol. 5, no. 1, Jul. 2021.
@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},
  }