London Text Analytics: call for venues and speakers

After a brief hiatus, I’m pleased to say that we will shortly be relaunching the London Text Analytics meetup. As many of you know, in the recent past we have organized some relatively large and ambitious events at a variety of locations. But we have struggled to find a regular venue, and as a result have had difficulty in maintaining a scheduled programme of events.

What we really need is a venue we can use on a more regular schedule, ideally on an ex-gratia basis. It doesn’t have to be huge – in fact; a programme of smaller (but more frequent) meetups is in many ways preferable to a handful of big gatherings.

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Text Mining and Visualization

I received a pleasant surprise in the post today: my personal copy of Text Mining and Visualization: Case Studies Using Open-Source Tools, edited by Markus Hofmann and Andrew Chisholm. Now I don’t normally blog about books, since as editor of Informer there was a time when I would be sent all manner of titles for inspection and review. But I’ll make an exception here. This is partially since Chapter 7 is my own contribution (on mining search logs), as discussed in my earlier blog posts. This is complemented by 11 other chapters, covering a variety of topics organised into four sections:

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Extracting sentiment from healthcare survey data

A short while ago I posted the slides to Despo Georgiou’s talk at the London Text Analytics meetup on Sentiment analysis: a comparison of four tools. Despo completed an internship at UXLabs in 2013-4, and I’m pleased to say that the paper we wrote documenting that work is due to be presented and published at the Science and Information Conference 2015, in London. The paper is co-authored with my IRSG colleague Andy MacFarlane and is available as a pdf, with the abstract appended below.

As always, comments and feedback welcome 🙂

ABSTRACT

Sentiment analysis is an emerging discipline with many analytical tools available. This project aimed to examine a number of tools regarding their suitability for healthcare data. A comparison between commercial and non-commercial tools was made using responses from an online survey which evaluated design changes made to a clinical information service. The commercial tools were Semantria and TheySay and the non-commercial tools were WEKA and Google Prediction API. Different approaches were followed for each tool to determine the polarity of each response (i.e. positive, negative or neutral). Overall, the non-commercial tools outperformed their commercial counterparts. However, due to the different features offered by the tools, specific recommendations are made for each. In addition, single-sentence responses were tested in isolation to determine the extent to which they more clearly express a single polarity. Further work can be done to establish the relationship between single-sentence responses and the sentiment they express.

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Sentiment analysis: a comparison of four tools

Diana Maynard entertains the troops

Last week I had the privilege of organising the 13^th meeting of the London Text Analytics group, which featured two excellent speakers: Despo Georgiou of Atos SE and Diana Maynard of Sheffield University. Despo’s talk described her internship at UXLabs where she compared a number of tools for analysing free-text survey responses (namely TheySay, Semantria, Google Prediction API and Weka). Diana’s talk focused on sentiment analysis applied to social media, and entertained the 70+ audience with all manner of insights based on her expertise of having worked on the topic for longer than just about anyone I know. Well done to both speakers!

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Sentiment analysis tools for non-coders?

I have an intern who will shortly be starting a project to extract sentiment from free text survey responses from the healthcare domain. She doesn’t have much programming experience, so is ideally looking for a toolkit /platform that will allow her to experiment with various approaches with minimal coding (e.g. perhaps just some elementary scripting etc.).

Free is best, although a commercial product on a trial basis might work. Any suggestions?

Related Posts:

1. How do you compare two text classifiers?
2. Text Analytics Summit Europe – highlights and reflections
3. How do you measure site search quality?
4. Prostitutes Appeal to Pope: Text Analytics applied to Search
5. The role of Natural Language Processing in Information Retrieval

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Text Analytics Summit Europe – highlights and reflections

Earlier this week I had the privilege of attending the Text Analytics Summit Europe at the Royal Garden Hotel in Kensington. Some of you may of course recognise this hotel as the base for Justin Bieber’s recent visit to London, but sadly (or is that fortunately?) he didn’t join us. Next time, maybe…

Still, the event was highly enjoyable, and served as visible testament of increasing maturity in the industry. When I did my PhD in natural language processing some *cough* years ago there really wasn’t a lot happening outside of academia – the best you’d get in mentioning ‘NLP’ to someone was an assumption that you’d fallen victim to some new age psychobabble. So it’s great to see the discipline finally ‘going mainstream’ and enjoying attention from a healthy cross section of society. Sadly I wasn’t able to attend the whole event, but  here’s a few of the standouts for me:

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