Sentiment Analysis of Tweets Using Hadoop


  • Pranav Seth
  • Apoorv Sharma
  • R Vidhya





Sentiment Analysis, tokens, tweets, Hadoop, emotion.


Blogging and networking platforms like Facebook, Reddit, Twitter and LinkedIn are social media channels where users can share their thoughts and opinions. Since online chatter is a vital and exhaustive source of information, these thoughts and opinions hold the key to the success of any endeavour. Tweets which are posted by millions all over the world can be used to analyse consumers’ opinions about individual products, services and campaigns. These tweets have proven to be a valuable source of information in the recent years, playing key roles in success of brands, businesses and politicians. We have tackled Sentiment Analysis with a lexicon-based approach for extracting positive, negative, and neutral tweets by using part-of-speech tagging from natural language processing. The approach manifests in the design of a software toolkit that facilitates the sentiment analysis. We collect dataset, i.e. the tweets are fetched from Twitter and text mining techniques like tokenization are executed to use it for building classifier that is able to predict sentiments for each tweet.




[1] K. Ghag and K. Shah, “Comparative analysis of the techniques for sentiment analysis,†in Proc. Int. Conf. Advances in Technology and Eng., pp. 1–7, Jan. 2013.

[2] M. Bouazizi and T. Ohtsuki, “Sentiment Analysis: from Binary to Multi-Class Classification - A Pattern-Based Approach for Multi-Class Sentiment Analysis in Twitter,†in Proc. IEEE ICC, May 2016.

[3] M. Bouazizi and T. Ohtsuki, “Sentiment Analysis in Twitter: from Classification to Quantification of Sentiments within Tweets,†in Proc. IEEE ICC, May 2016.



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How to Cite

Seth, P., Sharma, A., & Vidhya, R. (2018). Sentiment Analysis of Tweets Using Hadoop. International Journal of Engineering & Technology, 7(3.12), 434–436.
Received 2018-07-23
Accepted 2018-07-23
Published 2018-07-20