A Scalable, Lexicon Based Technique for Sentiment Analysis
This work addresses the need for scalable sentiment analysis tools for big data applications, though it appears incremental as it applies existing methods to new data.
The researchers tackled the challenge of performing sentiment analysis on large-scale social media data by developing a lexicon-based technique using Hadoop, achieving high efficiency in both speed and accuracy on a dataset of tweets.
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.