Sentiment Analysis on Tweets

Authors(2) :-K. Amaravathi, N. Lokeswari

The rapid increase of textual data is overwhelming. The huge amount of data generated from the different call sites, customer reviews on different products, and so on is in the unstructured form. So the amount of textual centre logs, emails, blogs, documents on the internet, tweets form twitter, customer comments from different social networking data rapidly increasing makes the summarization task challenging for businesses. In this paper we provide techniques that how to organize textual data and analyze textual data for extracting intuitive customer intelligence from a large collection of data. The predominant and different types of information on twitter make it one of the most appropriate virtual environments for information monitoring and tracking. In this paper, starting with the analysis of different hash-tags, categorization of different areas, identification of influence, and finally analysis of sentiment. This paper tries to draw some important conclusions based on the sentiment analyzed.

Authors and Affiliations

K. Amaravathi
CSE, Anil Neerukonda Institute of Technology & Sciences, Visakhapatnam, Andhra Pradesh, India
N. Lokeswari
CSE, Anil Neerukonda Institute of Technology & Sciences, Visakhapatnam, Andhra Pradesh, India

Data Mining, Machine Learning, Natural Language Processing, Twitter, Prediction.

  1. https://www.analyticsvidhya.com/blog/2016/04/complete-tutorial- tree-based-modeling-scratch-in-python/
  2. https://machinelearningmastery.com/naive-bayes-for-machine- learning/
  3. https://rpubs.com/aka7h/simple-sentiment-analysis 
  4. Jiawei Han, Micheline Kamber and Jian Pei, "Data Mining - Concepts and Techniques" (3rd edition)
  5. Amit G. Shirbhate1 , Sachin N. Deshmukh, "Feature Extraction for Sentiment Classification on Twitter Data", International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor (2014): 5.611
  6. Chakraborty, Goutam, and Murali Krishna Pagolu. "Analysis of unstructured data: Applications of text analytics and sentiment mining." In SAS global forum, pp. 1288-2014. 2014.
  7. Huq, Mohammad Rezwanul, Ahmad Ali, and Anika Rahman. "Sentiment Analysis on Twitter Data using KNN and SVM." IJACSA International Journal of Advanced Computer Science and Applications 8, no. 6 (2017): 19-25.

Publication Details

Published in : Volume 4 | Issue 8 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 20-26
Manuscript Number : IJSRSET151366
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

K. Amaravathi, N. Lokeswari, " Sentiment Analysis on Tweets, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 8, pp.20-26, May-June.2018
URL : http://ijsrset.com/IJSRSET151366

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