Volume 31, Issue 3 (IJIEPR 2020)                   IJIEPR 2020, 31(3): 343-350 | Back to browse issues page


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Sasikanth K, Samatha K, Deshai N, Sekhar B V D S, Venkatramana S. Effective Sentiment Analysis on Twitter with Apache Spark. IJIEPR. 2020; 31 (3) :343-350
URL: http://ijiepr.iust.ac.ir/article-1-1067-en.html
1- Department of CSE, GITE, Rajahmundry, A.P, India , sasikanth@giet.ac.in
2- 2. Department of CSE, JNTUK, Kakinada, A.P, India
3- 3. Department of IT, SRKREC, Bhimavaram, A.P, India
Abstract:   (538 Views)
The Today’s interconnected world generates huge digital data, while millions of users share their opinions, feelings on various topics through popular applications such as social media, different micro blogging sites, and various review sites on every day. Nowadays Sentiment Analysis on Twitter Data which is considered as a very important problem particularly for various organizations or companies who want to know the customers feelings and opinions about their products and services. Because of the data nature, variety and enormous size, it is very practical for several applications, range from choice and decision creation to product assessment. Tweets are being used to convey the sentiment of a tweeter on a specific topic. Those companies keeping survey millions of tweets on some kind of subjects to evaluate actual opinion and to know the customer feelings. This paper major goal would be to significantly collect, recognize, filter, reduce and analyze all such relevant opinions, emotions, and feelings of people on different product or service could be categorized into positive, negative or neutral because such categorization improves sales growth about a companychr('39')s products or films, etc. We initiate that the Naïve Bayes classifier be the mainly utilized machine learning method for mining feelings from large data like twitter and popular social network because of its more accuracy rates. In this paper, we scrutinize sentiment polarity analysis on Twitter data in a distributed environment, known as Apache Spark.
Full-Text [PDF 467 kb]   (252 Downloads)    
Type of Study: Research | Subject: Logistic & Apply Chain
Received: 2020/05/3 | Accepted: 2020/05/3 | Published: 2020/05/3

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