Data sparsity for twitter sentiment analysis in real-time from biased and noisy data

2021 ◽  
Vol 24 (8) ◽  
pp. 2403-2413
Author(s):  
Richa Rawal ◽  
Devesh Kumar Bandil ◽  
Srawan Nath
2021 ◽  
Vol 1 (2) ◽  
pp. 9-15
Author(s):  
V Mareeswari ◽  
Sunita S Patil ◽  
Ramanan G

Sentiment Analysis is becoming the field of focus with time considering the user experience weighs much more for the business to grow and for the studies as well. The sentimental expressions refers to the emotions or feeling of a person across certain point of focus or issues. So, in this project, with the assistance of Apache Spark Framework, an open source data streaming and processing platform, sentiment evaluation is done on the tweets from Twitter by the means of real time processing as well as an Ad-hoc Run. Some preprocessing of the textual data has been done upon for better characteristics extraction thus resulting in greater accuracy. The validation of this has been done for achieving better result by comparing the other processes when Naive Bayes algorithm is used.


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