BibTeX:

@article{IJIRSTV1I11036,
     title={BIG DATA SENTIMENT ANALYSIS USING HADOOP},
     author={Merin K Kurian, Vishnupriya S, Ramesh R, Divya G and Divya D },
     journal={International Journal for Innovative Research in Science & Technology},
     volume={1},
     number={11},
     pages={92--96},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV1I11036.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Social media gives users a platform to communicate effectively with friends, family, and colleagues, and also gives them a platform to talk about their favourite (and least favourite brands). This “unstructured” conversation can give businesses valuable insight into how consumers perceive their brand, and allow them to actively make business decisions to maintain their image. Rapid increase in the volume of sentiment rich social media on the web has resulted in an increased interest among researchers regarding Sentimental Analysis and Opinion Mining. However, with so much social media available on the web, Sentiment Analysis is now considered as a Big Data task. The main focus of the research was to find such a technique that can efficiently perform Sentiment Analysis on Big Data sets. In this paper Sentiment Analysis was performed on a large data set of tweets using Hadoop and the performance of the technique was measured in form of speed and accuracy. The experimental result shows that the technique exhibits very good efficiency in handling big sentiment data sets.

Big Data; Hadoop; Lexicon; Machine learning; Negation; NLP; Sentiment Analysis