A Survey On: Sentiment Analysis framework of Twitter data Using Classification

  • Pratima Deshapnde
  • Purva Joshi
  • Pratiksha Pawar
  • Diptee Madekar
  • M.D. Salunke


 Sentiment means classify the opinions which are in the form of text. As we are classifying the text it must having different unstructured contains with information of particular subject. There are various social media sites which gives us information with their thoughts but twitter is biggest among them where anyone who having account on twitter can tweet their opinions of any subject in any certain or uncertain way. Hence, we get extra scope in mining of such data. The analysis is done with the help of machine learning algorithms on the dataset. Classification is done by the classifier algorithms. The reviews data is used for performing sentimental analysis. This paper gives idea about how the analysis is done on twitter data by using various algorithms and machine learning concepts. It is a survey of different papers for analyzing the sentiments of text. 

Keywords: machine learning; sentiment analysis; Naïve Bayes; support vector machine; random forest.


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[1] Medha Khuran, et al. “Sentiment Analysis Framework of Twitter Data using Classification”, 5th IEEE International Conference,2018.

[2] P. Garg, H. Garg, and V Ranga, "Sentiment analysis of the Uri terror attack using Twitter," Computing, Communication and Automation (ICCCA), 2017.

[3] K. Lavanya and C. Deisy. "Twitter sentiment analysis using multi-class SVM," Intelligent Computing and Control (I2C2), International Conference on. IEEE, 2017.

[4] Ahuja, Shreya, and G. Dubey, "Clustering and sentiment analysis on Twitter data," 2nd International Conference on Telecommunication and Networks (TEL-NET), IEEE, 2017.

[5] M. Trupthi, , S. Pabboju, and G. Narasimha. "Sentiment analysis on twitter using streaming API," Advance Computing Conference (IACC), IEEE 7th International. IEEE, 2017.

[6] P. Huma, and S. Pandey, "Sentiment analysis on Twitter Data-set using Naive Bayes algorithm," Applied and Theoretical Computing and Communication Technology (iCATccT), 2nd International Conference on. IEEE, 2016.

[7] Zvarevashe, Kudakwashe, and Oludayo O. Olugbara, "A framework for sentiment analysis with opinion mining of hotel reviews," In Information Communications Technology and Society (ICTAS),2018 Conference, pp. 1-4. IEEE, 2018

[8] N. Zamani, M. Azminam, "Sentiment analysis: Determining people's emotions in facebook," University Teknologies MARA,Malaysia 2013.

[9] C. Aggarwal, and C. Xiang Zhai. "A survey of text classification algorithms," Mining text data. Springer, Boston, MA, pp. 163-222, 2012.

[10] Kaur, Harpreet, and Veenu Mangat, "A survey of sentiment analysis techniques," I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(ISMAC), International Conference, pp. 921-925, IEEE, 2017.

[11] Li, Jie, and Lirong Qiu, "A Sentiment Analysis Method of Short Texts in Microblog," Computational Science and Engineering (CSE) and Embedded and Ubiquitous Computing (EUC), IEEE International Conference, vol. 1, pp. 776-779, IEEE, 2017.

[12] https://www.kdnuggets.com/2018/03/5-things-sentiment-analysis-classification.html

[13] Sheeba Naz , Aditi Sharan , Nidhi Malik “sentiment classification on twitter data using support vector machine”, 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)

[14] Huma Parveen , Prof. Shikha Pandey “Sentiment Analysis on Twitter Data-set using Naïve Bayes Algorithm”, IEEE 2016.

[15] Rasika Wagh, Payal Punde, “Survey on Sentiment Analysis using Twitter Dataset”, Proceedings of the 2nd International conference on Electronics, Communication and Aerospace Technology (ICECA 2018) IEEE Conference IEEE.

[16] Alaa S. Al Shammari, “Real-time Twitter Sentiment Analysis using 3-way Classifier”, 2018 IEEE.

[17] Sahar A. El_Rahman, Feddah Alhumaidi AlOtaibi, Wejdan Abdullah AlShehri, “Sentiment Analysis of Twitter Data”, 2019 IEEE.

[18] Rincy Jose, Varghese S Chooralil,, “Prediction of Election Result by Enhanced Sentiment Analysis on Twitter Data using Classifier Ensemble Approach”, IEEE 2016.

[19] Ali Hasan 1, Sana Moin 1, Ahmad Karim 2 and Shahaboddin Shamshirband “Machine Learning-Based Sentiment Analysis for Twitter Accounts”, Math. Comput. Appl. 2018, 23, 11.

[20] M. Saif, "Sentiment analysis: Detecting valence, emotions, and other
affectual states from text," Emotion measurement, pp. 201-237, 2016.
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How to Cite
Deshapnde, P., Joshi, P., Pawar, P., Madekar, D., & Salunke, M. (2020). A Survey On: Sentiment Analysis framework of Twitter data Using Classification. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 5(3), 118-122. Retrieved from https://asianssr.org/index.php/ajct/article/view/935

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