I’m currently working on the scope of the 2nd edition of ‘Enterprise Search’ and am aware of the very blurred boundary between search and text analytics. Sentiment analysis is a good example. A search application could locate references to specific products in a repository of call centre transcripts but would not be able to indicate whether over time customers were more positive about the performance of the product, to use a very simplistic example. This is where sentiment analysis (sometimes referred to as ‘opinion mining’) has a very important role to play.

Yesterday over 80 attendees listened to two excellent presentations on sentiment analysis at a meeting of the London Text Analytics Meetup Group, sponsored by UXLabs. After excellent refreshments courtesy of the Financial Times, the first speaker was Despo Georgiou, currently a Business Consultant at Atos SE. Her recent MSc dissertation at City University was an examination of the value of two commercial sentiment analysis applications (Semantria and TheySay) and two non-commercial applications (Google Prediction API and WEKA) in analysing documents from the health care sector. This was a very good general introduction on how to conduct application assessments.

She was followed by Dr. Diana Maynard, a Research Fellow in the Computer Science department of Sheffield University. Diane talked mainly about sentiment analysis applied to social media but in a concluding Q&A session proved able to provide a tremendous amount of insight into all aspects of sentiment analysis. Do take a look at Diane’s blog!

For me it was a very valuable meeting and put a lot of fragmented knowledge I have of this area into context. It was the 13th meeting of the Text Analytics Meetup but the first I had attended as a member, and I am looking forward to future meetings. If you want to know more about sentiment analysis a good place to start is to download both the short book on Sentiment Analysis and Opinion Mining by Bing Liu and a recent paper by Eric Cambria on New Avenues in Opinion Mining and Sentiment Analysis.

Martin White