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

[Alexander Osherenko]

Sure, you have to consider the context of discussion. In my PhD thesis
(that you probably didn't read) I analyze affect of dialogue utterances and
use the chain of adjacent utterances as data for analysis. In this way, you
can analyze affect of separate utterances in the context of previous
utterances. AO

2011/12/16 Amanda Schiffrin <a.schiffrin at gmail.com>

> I think Justin has hit the nail on the head here.  I worked on an attempt
> to develop a sentiment detection module for a text analytics software
> system in my previous job, and I soon realised that once you start working
> with real data, both statistical and grammatical ('semantic') approaches
> will fail.  You need a more complex model of information in order to be
> able to understand that a tweet such as "Bummer, I left my iPhone on the
> bus - I'm lost without it :-(", despite containing only indicators of
> negative sentiment at the lexical level, still expresses high positive
> sentiment toward the *product*.  Being able to distinguish this kind of
> sentiment is one of the main drivers of commercial sentiment detection, and
> I'd say we're still a very long way away from anything like that level of
> sophistication.
>
> Mandy Schiffrin
>
>
> On 16 December 2011 20:24, Justin Washtell <lec3jrw at leeds.ac.uk> wrote:
>
>> "I would be very sad if this movie did not win a prize."
>>                        high_neg
>> "I'm very happy that the other reviewers have seen this movie for what it
>> is: rubbish."         high_pos
>>
>> Rather than (unfairly) singling out this system, I think these examples
>> serve to highlight that this is a very difficult (if not impossibly
>> ill-defined) problem. One cannot just assess the polarity of a statement -
>> one needs to know something about what the object of interest is. In the
>> above cases we are probably interested in [the writer's opinion of] the
>> movie... but that fact is of course *pragmatic* information.
>>
>> I'm out of my depth now, so I'll say no more :-) No doubt much has been
>> written on these issues.
>>
>> Justin Washtell
>> University of Leeds
>>
>> ________________________________________
>> From: corpora-bounces at uib.no [corpora-bounces at uib.no] On Behalf Of Angus
>> Grieve-Smith [grvsmth at panix.com]
>> Sent: 16 December 2011 17:25
>> To: corpora at uib.no
>> Subject: Re: [Corpora-List] EmoText - Software for opinion mining and
>> lexical affect sensing
>>
>> On 12/16/2011 9:01 AM, Alexander Osherenko wrote:
>> > You didn't test the approach for complex sentences. I always used the
>> > example "I am very sad if ..."
>>
>>     I don't want to nitpick, but that's not a very nativelike example
>> for a test sentence.  I've only heard English speakers use "I am very
>> sad if ..." in habitual or generic contexts, and even then "I get very
>> sad when ..." is much more common.  "I would be very sad if ..." is also
>> used.  Maybe check your test sentences against the CoCA or something?
>>
>> --
>>                                -Angus B. Grieve-Smith
>>                                grvsmth at panix.com
>>
>>
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