Twitter Sentiment Analysis:
The fundamental issue with opinion mining is to select the right approach for right problem to solve. The common approaches used for opinion mining are
> parts of speech
> n-grams using thesaurus and
> text mining
Each of these approaches is used for a specific problem, for example to know the general mood of a population on some incidence or news/event, or to know sentiment for a market ,stock or company we may use the parts of speech. But we might not use parts of speech for an issue for which the thesaurus is not built yet. When we need to capture and develop thesaurus for that particular issue n-grams approach will be a good fit. For some other problems we might need to perform text mining, like clustering and classification. The bottom-line is no single approach solves all problems.
At Predictive Analytics we have developed a novel mechanism to handle the twitter sentiment analysis problem for all above listed approaches. This technique produces exceptionally good results (less false positives). The industry standard in twitter sentiment analysis is roughly 67% accuracy (rate of false positives) where as our technique generates over 87% correct results (false positive rate less than 13%).
How it is different and why it produces exceptional result please give us a call for a free demo.
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