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

[Alexander Osherenko]

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 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>

> 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 [corpora-bounces at uib.no] On Behalf Of
> Alexander Osherenko [osherenko at gmx.de]
> Sent: 16 December 2011 08:46
> To: 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>.
>
> I apologize for some advertising.
>
> Kind regards
> Alexander Osherenko
>
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