What Type of Sentiment Analysis Can I Measure?
Sentiment analysis is a data mining application that uses natural language processing and text analysis to measure different parameters on the web. With the assistance of text analytics it can continually measure positive and negative language being said about a brand or organization from social media feeds, blog posts, reviews, and news articles. And now with the addition of Webhose’s historical archive, it is also used in the financial industry for predictive analysis.
Many sentiment analysis platforms measure the general sentiment of a text. Webhose’s sophisticated algorithm, however is capable of unprecedented granularity. For example, a data analysis service customer of ours was able to use our analyses to search for customer reactions at the phrase level to understand more about the different experiences of the customers. Restaurant managers have used it to understand how their customers feel about different individual parameters such as ambiance, service, food, price and location. The process can be copied to serve range of consumer verticals, including retail, hospitality, electronics, automotive or telecommunications industries.
Collecting the Data for Measuring Sentiment Analysis
One application of machine learning is sentiment classification. Sentiment classification is successful when you have both a machine learning engine as well as a mass of structured data that allows for training of the machine learning engine.
The datasets needed to train or test for sentiment analysis should include two pairs split in an 80% to 20% ratio into two separate subsets, including an additional test dataset. Many free datasets for sentiment analysis are available on the web from major organizations, universities and networking sites like Twitter and Facebook but they are often short and basic.
Alternatively, you could also use Webhose’s free datasets which include news articles (available in 12 languages and covering a variety of categories), online discussions, blogs and news articles according to virality and data on different major organizations (such as Facebook, Apple, Amazon and Google). You can also use our historical archive going back as far as 2008 to create your own dataset based on a set of advanced filters. We have also gathered millions of rated reviews so that anyone can use these structured datasets for sentiment classification.
Real World Applications of Sentiment Analysis
Customer sentiment is vital for organizations looking to improve their marketing campaigns, better train their salesmen, or tweak their marketing. It has additional applications as well. For instance, it has been used in Microsoft Research Labs to determine which women were at risk for postnatal depression via their Twitter posts.
Audio sentiment analysis measures the calls of customers to determine the sentiment of callers and to try to prevent any problems before things get worse. Assuming you have a massive enough set of data, sentiment analysis can be used to gauge a fairly accurate range of positive and negative emotion. Sentiment analysis is continuing to be developed and applied in different ways across many industries.
Want to Learn More?
Learn more about how Webhose’s online discussions data feed can perform sentiment analysis on a wide variety of social media platforms, message boards and review sites to gauge trends on the different channels.