[Corpora-List] EmoText - Software for opinion mining and lexical affect sensing

[Justin Washtell]

Hi Alexander,

Thanks for the very attentive and informative response. There is no doubt that this is a tough problem area, and I have absolute respect for anybody working towards a solution. Of course, without having yet read your publications I cannot fully appreciate the gains that you have made. But I think therein lies the point I was really alluding to...

The demos as they presently stand seem at odds with the very strongly worded "sales pitch" of your site. Partly perhaps because they are difficult for the non-technical user to understand, and partly perhaps because of they are so easily thwarted. No doubt this performance is indeed hampered by some of the limitations you have just identified, but you do present them as a product rather than as a work-in-progress.

Might it be better to put these particular demos on a more honest and informative site aimed at a technical/academic audience (i.e. where you can freely to acknowledge their limitations). And then - while I am somewhat loath to encourage it - you could conjure up something that is a little more "on rails" for your business site?

Justin Washtell
University of Leeds

________________________________________
From: osherenko at gmail.com [osherenko at gmail.com] On Behalf Of Alexander Osherenko [osherenko at gmx.de]
Sent: 16 December 2011 14:01
To: Justin Washtell
Cc: Corpora at uib.no
Subject: Re: [Corpora-List] EmoText - Software for opinion mining and lexical affect sensing

Hello Justin,

Thanks for your comments.

1. Statistical demo.

The goal is to measure the opinion of a reviewer for a particular movie review. It is typical to measure opinion using number of stars. You can go to www.reelviews.net<http://www.reelviews.net> and convince yourself.

Many part results show what you would get if you extract particular features. For example, you can extract stylometric features and get A stars. You can extract lexical features and get B stars. The final result (majority or average) is calculated on the basis of the part results.

You can extract stylometric, deictic, grammatical, lexical features calculated on the basis of the review and analyze your review using, for example, NaiveBayes. As a part result you can also optimize feature space or fuse results using BayesNet.

2. Semantic demo.

I don't think you are doing something wrong. But you have to know: it is only a demo and BTW I also want to learn something. :) This demo relies on theoretical findings of Leech and Svartvik "A Communicative Grammar of English" and has to be extended to analyze real-life utterances. Hence, your examples are very helpful.

I don't want to show how many words I use for analysis. Although I use about 4000 words it is not enough. "I'm fairly unimpressed" -- the word "unimpressed" is not in the dictionary that's why "only" neutral. You might want to try "It is not good" or "It is good" and its variants if it is not too trivial for you. Big dictionaries are not the issue because I can extend my dictionaries accordingly. I also didn't use big slang dictionaries.

In contrast, I want to show that combinations of negations, intensifiers, emotion words are sufficient to analyze affect. For example, in the example "This demo is far from brilliant." the word "far" can be considered as negation and the combination <negation><emotion word> calculates the desired meaning. In other example "couldn't be better", there is something that concerns comparative and has to studied more thoroughly in future.

You didn't test the approach for complex sentences. I always used the example "I am very sad if ..."

Best
Alexander

2011/12/16 Justin Washtell <lec3jrw at leeds.ac.uk<mailto:lec3jrw at leeds.ac.uk>>
Hello Alexander,

I tried both of your demos out of interest.

For the first demo I used the default options (the movie reviews and Naive Bayes). I did not understand the output, or how it was supposed to relate to the various parts of the input (if indeed it is?)

For the second demo I entered the following sentences and received the following classifications:

This demo is terrible.                          low_neg
This demo is no good at all.                    high_pos
This demo is far from brilliant.                low_pos

This demo is excellent.                         low_pos
This demo is not bad at all.                    low_pos
Thid demo couldn't be better!                   low_neg

Am I doing something wrong?

I would presently dispute your claimed "undisputable advantages". I am not sure whether your intended customers - who are presumably not language technology experts - will require less or more convincing.

Justin Washtell
University of Leeds

________________________________________
From: corpora-bounces at uib.no<mailto:corpora-bounces at uib.no> [corpora-bounces at uib.no<mailto:corpora-bounces at uib.no>] On Behalf Of Alexander Osherenko [osherenko at gmx.de<mailto:osherenko at gmx.de>]
Sent: 16 December 2011 08:46
To: Corpora at uib.no<mailto:Corpora at uib.no>
Subject: [Corpora-List] EmoText - Software for opinion mining and lexical       affect sensing

Dear all!

Recently I made an announcement of a book about opinion mining and lexical affect sensing. In this contribution I would like to point you to the EmoText demo program that relies on the findings in this book. It was implemented for the European CALLAS project.

The link is:
www.socioware.de/products.html<http://www.socioware.de/products.html><http://www.socioware.de/products.html>.

I apologize for some advertising.

Kind regards
Alexander Osherenko


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