STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Computer Science Languages Review Article

Sentimental Analysis in Twitter Using Python

  1. Rukmini Ankush Dhamdhere Department of Information Technology, NBN Sinhgad School of Engineering, Pune
  2. Nikita Makarand Dhumal Department of Information Technology, NBN Sinhgad School of Engineering, Pune
  3. Mayuri Keshav Gawali Department of Information Technology, NBN Sinhgad School of Engineering, Pune
  4. Ganesh Pramod Kulkarni Department of Information Technology, NBN Sinhgad School of Engineering, Pune

Abstract

Social media websites are a great source of information because they have a lot of data. For instance, Twitter generates millions of packets of text. These statistics may be employed for commercial or charitable purposes. One of the hottest new buzzwords for many business strategies is the analysis of data from these social networking websites. Sentimental analysis can be used to manage election campaigns, global health problems, technical concepts, inventions, entertainment, and natural resource issues. Using Stanford NLP Libraries implemented in SaaS (cloud), which will manage all global current affairs, our proposed study assesses sentimental analysis of Twitter data. Implementing the cloud will improve speed to market, result growth, and process efficiency.

Keywords

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