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• W2057232472 abstract "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. Hence the conventional sentiment analysis approaches fails to efficiently handle the vast amount of sentiment data available now a days. The main focus of the research was to find such a technique that can efficiently perform sentiment analysis on big data sets. A technique that can categorize the text as positive, negative and neutral in a fast and accurate manner. In the research, 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 results shows that the technique exhibits very good efficiency in handling big sentiment data sets." @default.
• W2057232472 created "2016-06-24" @default.
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• W2057232472 date "2014-10-08" @default.
• W2057232472 modified "2023-09-27" @default.
• W2057232472 title "A Scalable, Lexicon Based Technique for Sentiment Analysis" @default.
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