EmoClass stringclasses 10
values | EmoAct float64 1 7 | EmoVal float64 1 7 | EmoDom float64 1 7 | SpkrID int64 1 3.33k | Gender stringclasses 2
values | Split_Set stringclasses 2
values | transcript stringlengths 1 353 ⌀ | emotion2vec_features listlengths 1 1 |
|---|---|---|---|---|---|---|---|---|
O | 4 | 4.2 | 3.6 | 1,391 | Female | Train | curious about how surgeries were booked. | [
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... |
X | 3.4 | 4.6 | 4.4 | 1,391 | Female | Train | so for example, if you're performing an appendectomy- | [
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N | 4.6 | 3.4 | 4.4 | 1,391 | Female | Train | your last 10 were particularly difficult procedures and then your next one is not that difficult, that- | [
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O | 3.8 | 3.4 | 3.4 | 1,391 | Female | Train | can really affect the booking time and how close the booking time is to the actual time. | [
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N | 4 | 4.2 | 4 | 1,391 | Female | Train | and those overtimes cascade until the end of the operating room day when you're running an- | [
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X | 3.4 | 3.8 | 3.8 | 1,391 | Female | Train | hour overtime and you have to pay nurses overtime and there's a cost associated with that. | [
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"feats": [
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... |
X | 4 | 4.8 | 4.2 | 1,391 | Female | Train | yeah, that isn't terribly efficient. | [
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O | 4 | 4.4 | 4.2 | 1,391 | Female | Train | and also it looks at the procedures within a given day to make sure over the day the operating room runs on time and not just that one specific procedure. | [
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X | 4 | 3.6 | 4 | 1,391 | Female | Train | to make sure this wasn't a punitive thing and this is purely just to optimize performance. | [
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H | 5 | 5.4 | 5 | 1,391 | Female | Train | right. not to see who the slowest surgeon in the west of gta is. | [
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"feats": [
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... |
H | 2.2 | 4.8 | 4 | 1,391 | Female | Train | so there is a google or-tools, which is an open-source software suite developed by the ai division of google. and i just used that and created a python algorithm- | [
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... |
N | 3.8 | 4.6 | 4 | 1,391 | Female | Train | that would analyze the three years of data we were given- | [
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... |
N | 4.4 | 3.8 | 4.2 | 1,391 | Female | Train | which is surgeon-specific and it would look at how long each procedure took. | [
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... |
H | 4 | 4.4 | 3.8 | 1,391 | Female | Train | python algorithm, it was a really simple, maybe 200-line script created with the coding language python. | [
{
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N | 4 | 4.8 | 4 | 1,391 | Female | Train | very readable. and it's actually open-source. | [
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N | 4.6 | 4.8 | 4.6 | 1,391 | Female | Train | yes. all open-source. that was a priority because we wanted it to be accessible. | [
{
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N | 3.8 | 4.2 | 4 | 1,391 | Female | Train | this is a new emerging technology and we really wanted to encourage the- | [
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X | 3.2 | 4.8 | 3.8 | 1,391 | Female | Train | adaptation of this technology and allow people to see its potential. | [
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... |
H | 5.2 | 5.4 | 4.8 | 1,391 | Female | Train | this doesn't seem to be a very efficient way of calculating surgery times. | [
{
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... |
X | 4.8 | 4.6 | 4.6 | 1,391 | Female | Train | multiple divisions from the institution where we got the data and we were really hoping to see a reduction in overtime and undertime rates. | [
{
"feats": [
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... |
O | 4.8 | 3.4 | 4.4 | 1,391 | Female | Train | which means that we're running on time 40%- | [
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... |
H | 3.6 | 5 | 3.8 | 1,391 | Female | Train | i just thought we can write an algorithm and that'll be a fun side project. | [
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"feats": [
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... |
S | 3.4 | 3 | 4.2 | 1,391 | Female | Train | i thought no one wants to hear a 19-year-old who is still an undergrad talk about what she's learned, but- | [
{
"feats": [
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... |
H | 4.2 | 4.6 | 3.6 | 1,389 | Female | Train | my dad encouraged me to write the paper and it was a really great learning experience. | [
{
"feats": [
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... |
U | 5 | 4.4 | 4.4 | 1,389 | Female | Train | wow. you are impressive. so the actual writing of the paper, what- | [
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H | 4.4 | 4.8 | 4.2 | 1,391 | Female | Train | to learn a lot more about the healthcare industry and how in healthcare they're always pushing to make advancements and always trying to innovate and optimize the delivery. | [
{
"feats": [
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N | 3.8 | 4.2 | 4.4 | 1,391 | Female | Train | it was expedited since it is a covid related topic. | [
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N | 3.8 | 4.2 | 4.2 | 1,391 | Female | Train | so i've had one surgeon talk to me about testing it on their own data- | [
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N | 4.8 | 5 | 4.6 | 1,391 | Female | Train | their time raise. i've had feedback from people i know in the healthcare and tech industry... | [
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"feats": [
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H | 3.6 | 4.8 | 4 | 1,391 | Female | Train | yeah. and really make this more accessible if they integrate with their own technology. | [
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"feats": [
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H | 4 | 4.4 | 4 | 1,391 | Female | Train | so the python algorithm is open-source. i am grateful for them to bring me onto the project. | [
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H | 5 | 5.4 | 5 | 1,389 | Female | Train | the ultimate goal for me is always to learn, to push further. | [
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H | 4.2 | 4.4 | 4.2 | 1,389 | Female | Train | and really i didn't think i was capable of writing a paper. | [
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O | 4.8 | 4.6 | 5.6 | 1,389 | Female | Train | so what's next, other than working with this company? | [
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N | 3.2 | 3.8 | 3.6 | 1,389 | Female | Train | i hope more ai work. | [
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H | 3.8 | 4.6 | 3.8 | 1,389 | Female | Train | waterloo is what got me interested in machine learning and sort of understanding that it was a lot more accessible- | [
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H | 4.2 | 4.4 | 4.6 | 1,394 | Female | Train | just also with python and it really- | [
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H | 4 | 5 | 4.4 | 1,394 | Female | Train | to me that was really inspiring and encouraged me to sort of dream bigger. | [
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X | 4 | 4.4 | 4.2 | 1,394 | Female | Train | but i didn't really understand the technical components and i didn't really understand how i as an individual could be applying machine learning to solve all sorts of different problems. | [
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X | 4.2 | 4.8 | 4.2 | 1,394 | Female | Train | my surgeon dad was helpful because i didn't understand the healthcare industry. | [
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H | 4 | 5.4 | 4.8 | 1,394 | Female | Train | you should write a paper about your idea if you want to share it with the world and also don't think that people don't want to listen to what you have to say because- | [
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H | 3.8 | 5.4 | 4 | 1,389 | Female | Train | there are some really bright students that i've met in waterloo and- | [
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