Big data and sentiment analysis using KNIME: Online reviews vs. social media

Text analytics and sentiment analysis can help an organization derive potentially valuable business insights from text-based content such as word documents, email and postings on social media streams like Facebook, Twitter and LinkedIn. The system described here analyses opinions about various gadge...

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Bibliographic Details
Published in:	2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) pp. 1464 - 1468
Main Authors:	Minanovic, Ana, Gabelica, Hrvoje, Krstic, Zivko
Format:	Conference Proceeding
Language:	English
Published:	MIPRO 01-05-2014
Subjects:	Dictionaries Internet Media Sentiment analysis Tag clouds Twitter
Online Access:	Get full text
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Summary:	Text analytics and sentiment analysis can help an organization derive potentially valuable business insights from text-based content such as word documents, email and postings on social media streams like Facebook, Twitter and LinkedIn. The system described here analyses opinions about various gadgets collected from two different sources and in two different forms; online reviews and Twitter posts (tweets). Sentiment analysis can be applied to online reviews in easier and more detailed way than to the tweets. Namely, online reviews are written in clear and grammatically more accurate form, while in tweets, internet slang, sarcasm and allegory are often used. System described here explains methods of data collection, sentiment analysis process for online reviews and tweets using KNIME, gives an overview of differences and analysis possibilities in sentiment analysis for both data sources.
DOI:	10.1109/MIPRO.2014.6859797