The problem defined

Sentiment analysis, or mining for opinions – when we perform this type of project, multiple technologies can be leveraged, for example Natural Language Processing (NLP ), textual analysis, and computational linguistics, to identify and extract subjective material from a source. Sentiment analysis is and has been used in a variety of ways, and more opportunities appear every day. One interesting example is that it is used by political candidates and administrations in an attempt to monitor overall opinions about policy changes and campaign announcements, empowering them to fine-tune their approach and messaging to better relate to voters and constituents.

To further drive the point, in a recent blog posting, Mike Waldron, Head of Marketing and Sales at AYLIEN , said the following:

"As more and more content is created and shared online, through social channels, blogs, review sites and so on, we are becoming more and more vocal and open about our experiences online. In a recent study carried out by Zendesk, it was noted that 45% of people share bad customer service experiences and 30% share good customer service experiences via social media (https://www.zendesk.com/resources/customer-service-and-lifetime-customer-value/)", which again highlights" the need and desire for businesses to mine this information to gain business insight from it has also increased."
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