Dynamic Data Analysis and Decision Making on Twitter Data
Nikitha Kumari1, Prabhakar kandukuri2
1Nikitha Kumari*, Vardhaman College of Engineering, Hyderabad. Department of Computer Science and Engineering.
2Prabhakar kandukuri, Vardhaman College of Engineering, Hyderabad, Department of Computer Science and Engineering.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 4010-4045 | Volume-9 Issue-1, November 2019. | Retrieval Number: A5255119119/2019©BEIESP | DOI: 10.35940/ijitee.A5255.119119
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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: It became a tedious task for the data analysts to make decisions on social networks. The existing approaches are not adequate to perform data pre-processing, analysis and decision making on the data dynamically. Therefore, this research aims to propose an approach to data analysis and decision making. The proposed approach emphasis on extracting tweets form twitter API (Application Program Interface), pre-processing the tweets by following seven pre-processing steps. The processed tweets are trained by NLTK (Natural Language Toolkit) and Text Blob are given to the sentiment analysis. Classification is done using the Naive Bayes algorithm to make a decision on processed tweets. The tweets which are related to “MeToo Movement” are considered primarily for decision making and satisfactory results are obtained. It is been observed that the proposed approach is accurate when compared to other approaches.
Keywords: Twitter, Text Pre-processing, Machine learning, Sentiment Analysis, MeToo movement.
Scope of the Article: Machine learning