Twitter Sentiment and Sarcasm Analysis

References

[1] Alec Go (alecmgo@stanford.edu) Lei Huang (leirocky@stanford.edu) Richa Bhayani (richab86@stanford.edu) “Twitter Sentiment Analysis’’ June 6, 2009 Error! Hyperlink reference not valid.

[2] Efthymios Kouloumpis (epistimos@i-sieve.com) Theresa Wilson(taw@jhu.edu) Johanna Moore(j.moore@ed.ac.uk) “Twitter Sentiment Analysis: The Good the Bad and the OMG!” https://ojs.aaai.org/index.php/ICWSM/article/view/14185/14034

[3] David Zimbra, Ahmed Abbasi, Daniel Zeng, and Hsinchun Chen. “The State-of-the-Art in Twitter Sentiment Analysis: A Review and Benchmark Evaluation.” ACM Trans. Manage. Inf. Syst. 9, 2, Article 5 (August 2018), https://doi.org/10.1145/3185045

[4] Cristian R. Machuca, Cristian Gallardo and Renato M. Toasa Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series, Volume 1828, 2020 International Symposium on Automation, Information and Computing (ISAIC 2020) 2-4 December 2020, Beijing, China Citation Cristian R. Machuca et al 2021 J. Phys.: Conf. Ser. 1828 012104 https://iopscience.iop.org/article/10.1088/1742-6596/1828/1/012104/meta

[5] Lei Zhang, Riddhiman Ghosh, Mohamed Dekhil, Meichun Hsu, Bing Liu Hewlett-Packard Laboratories mails: {riddhiman.ghosh, mohamed.dekhil, meichun.hsu} @hp.com “Combining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis” University of Illinois at Chicago 1501 Page Mill Rd., Palo Alto, CA 851 S. Morgan St., Chicago, LL {lzhang3, liub}@cs.uic.edu https://www.hpl.hp.com/techreports/2011/HPL- 2011-89.pdf

[6] Ahmed Abbasi, Ammar Hassan, Milan Dhar E-mail: abbasi@comm.virginia.edu, mah9tg@virginia.edu, msd4ah@virginia.edu “Benchmarking Twitter Sentiment Analysis Tools” University of Virginia Charlottesville, Virginia, USA http://www.lrec-conf.org/proceedings/lrec2014/pdf/483_Paper.pdf

[7] Soujanya Poria , Devamanyu Hazarika, Navonil Majumder , Gautam Naik, Erik Cambria, Rada Mihalceaι Information Systems Technology and Design, SUTD, Singapore ΦSchool of Computing, National University of Singapore, Singapore Centro de Investigacion en Computaci ´ on, Instituto Polit ´ ecnico Nacional, Mexico ´ Computer Science & Engineering, Nanyang Technological University, Singapore Computer Science & Engineering, University of Michigan, USA,2019. “MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations”

[8] Abdullah Alsaeedi1 , Mohammad Zubair Khan2 Department of Computer Science, College of Computer Science and Engineering Taibah University, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 2, 2019. “A Study on Sentiment Analysis Techniques of Twitter Data”

[9] Brahmananda Reddy, D.N.Vasundhara, P. Subhash, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-2S11, September 2019 “Sentiment Research on Twitter Data”

[10] Mamta1, Ela Kumar2, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 06 | June 2019. “A Real-Time Twitter Sentiment Analysis and Visualization System: TwiSent”

@article{1703639,
      author = {Sarthak Tripathi, Ayushman Pandey, Anmol Chitransh, Prof S.P Medhane},
      title = {Twitter Sentiment and Sarcasm Analysis},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {1},
      pages = {251-259},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703639.pdf},
      abstract = {As we all are seeing that social media is now the biggest platform for showcasing our views and also our opinions in the support of any thought. But nowadays it is seen that most of the people didn?t get the tone of tweet and can misunderstand it. So now there is a biggest requirement to filter out the tone of our tweet i.e., we can classify our tweets into positive, negative or neutral. This project is basically based on the fact that we can use machine learning to filter out our tweets .We will extract the data from twitter and will store it in csv file, pre-process the data, then tokenize the data and using feature extraction will extract our features .Then using different machine learning algorithms such as Support Vector Machine (SVM) and Na?ve-Bayes Algorithm, polarities will be assigned to features which lies between -1 and 1.Based on value of polarity tweets will be classified as positive, negative or neutral. Machine Learning Algorithms such as Support Vector Machine, Naive Bayes Algorithm are used for sentiment classification. Support Vector Machine works mainly by investigating information and characterizing the components for calculation whereas Naive Bayes Algorithm works mainly using Bayes Theorem which is highly dependent on closeness of features. Many other tools such as Twitter Sentiment, SentiStrength etc. can be implemented but the best accuracy was given by Na?ve-Bayes Classifier.},
      month = {July},
  }