Sentimental Data Analysis for Prediction of Public Reaction Using Hadoop Framework
Abhinav Agarwal1, Aaditya Chaturvedi2, Priyanshi Singh3, S. Aarthi4
1Abhinav Agarwal, Department of Computer Science and Engineering, SRMIST, Chennai (Tamil Nadu), India.
2Aaditya Chaturvedi, Department of Computer Science and Engineering, SRMIST, Chennai (Tamil Nadu), India.
3Priyanshi Singh, Department of Computer Science and Engineering, SRMIST, Chennai (Tamil Nadu), India.
4S. Aarthi, Department of Computer Science and Engineering, SRMIST, Chennai (Tamil Nadu), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 252-255 | Volume-8 Issue-5, June 2019 | Retrieval Number: E6888068519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sentiment analysis is taken into account to be a sub-class of machine learning and natural language processing. It’s accustomed disencumber, identify, or depict opinions from completely diverse content structures, as well as news, reviews and editorials and sorts them as positive, neutral and negative. In this paper, we have an inclination towards investigating the effectiveness of linguistic possibilities for sensing the sentiment of Twitter messages. We have an inclination towards evaluating the usefulness of present lexical sources in addition to qualities that seize information regarding the natural and artistic language employed in microblogging. We take a administered attitude to the issue, however control current hashtags within the Twitter data for making training data. We are making use of Pig Latin in our system. We record the stream data and store it in .csv format file. Then we compare the words stored in file with AFINN dictionary and based upon the keywords provided, it will rate each keyword ranging from -5 to +5 depicting most negative to most positive comments. Those ratings are combined to obtain a numerical value and that is what gives us our prediction of public opinion.
Keywords: Terms: Sentimental Data Analysis, Public Reaction, Hadoop Framework, Piglatin, Natural Language Processing(NLP).
Scope of the Article: Data Analysis