I recently finished work on my text-based emotion detection engine and I am looking for other existing working systems to compare with mine in order to know what should be improved and also report comparisons in an upcoming paper.
I have come across many companies claiming to do emotion detection from text but only this one offers a demo that I can use to compare with my system: http://www.o2mc.io/portfolio-posts/text-analysis-restful-api-language-polarity-and-emotion/ (scroll all the way down to see the "try it yourself" section).
Please notice that I am not looking for polarity classification, which is the simpler task of saying if a text is positive or negative. What I am looking for is for emotions (sadness, anger, joy, etc...). Does anyone here know about any company/university/person offering a demo to such system?
As a reference, here is the link to my own system's demo:
http://demo.soulhackerslabs.com/emotion/
Your help is very much appreciated.
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I'm doing sentiment analysis on users' transcripts for UX website testing. I get the transcript from the testing session and then I analyze the transcript for sentiment analysis - what's the user's opinion about the website, what problems did the user encounter, whether he had any problems, got stuck, lost. Since this is quite domain-specific, I'm testing both TextBlob and Vader and see which gives better results. My issue is at the beginning of the process - the speech-to-text API's transcript isn't perfect. Sentences (periods) are not captured or are minimal. I'm not sure on what level the analysis should be since I was hoping I could do it on sentence-level. I tried making n-grams and analyzing those short chunks of text, but it isn't ideal and the results are slightly difficult to read - because there will be some parts that are repeated. Apart from this, I do classical text cleaning, tokenization, pos tagging, lemmatization and feed it to TextBlob and Vader.
Transcript example: okay so if I go just back over here it has all the information I need it seems like which is great so I'm pretty impressed with it similar to how a lot of government websites are set up over here it looks like I have found all the information I need it's a great website it has everything overall though it had more than enough information...
I did:
ngram_object = TextBlob(lines)
ngrams = ngram_object.ngrams(n=4)
which gives me something like (actually a WordList): [okay so if I, so if I go, if I go just...]
Then the results look like:
62 little bit small -0.21875 Negative
61 like little bit -0.18750 Negative
0 information hard find not see -0.291666667 Negative
1 hard find not see information -0.291666667 Negative
Is there a better way to analyze unstructured text in chunks rather than a full transcript?
This makes it difficult to capture what was the issue with the website. Changing the API isn't really an option since I'm working with something that was given to me to use as data collection for this particular sentiment analysis problem.
Any tips or suggestions would be highly appreciated, couldn't find anyone doing something similar to this.
I am not sure about what you really want but maybe you could take a look on speech sentiment analysis? I have read about RAVDESS, a database useful for sentiment classification. Take a look: https://smartlaboratory.org/ravdess/
Im really new to machine Learning.I have a project to identify a given sound.(Ex: cutting wood)In the audio clip there will be several sound. What i need to do is recognise that particular sound from it. I red some articles about machine learning. But i still have lack of knowledge where to start this project and also I'm running out of time.
Any help will be really appreciated. Can anyone please tell me how to do this?
Can i directly perform template(algorithms) matching for a sound?
It's a long journey ahead of you and Stack Overflow isn't a good place for asking such a generic question. Consult help section for more.
To get you started, here are some web sites:
Awesome Bioacoustic
Comparative Audio Analysis With Wavenet, MFCCs, UMAP, t-SNE and PCA
Here are two small repos of mine related to audio classification:
Gender classification from audio
Kiwi / not-a-kiwi bird calls detector
They might give you an idea where to start your project. Check the libraries I am using - likely they will be of help to you.
I have an alphabet which has not been tackled before, so when scanned, there's no way to detect the letters for recognition with OCR. I'm trying to program OCR for it, but don't have much experience in this. I'd appreciate some hints as to where to get started, and how such a system is normally implemented.
Take a look at this page--it describes the training process for an open source OCR engine.
The free Stanford Online Machine Learning class has a great set of lessons on Photo OCR in Part XVIII.
This blog post has a brief description of the example taught in the class.
There are some excellent resources at google books. Likewise, if you search for Optical Character Recognition on Amazon, there are some pretty up-to-date books that look to be fairly thick and intellectually challenging :D heh
btw - I'm well aware this post has some age, but you never know when some other person might stumble across this and find just what they need. And if this even has the chance of helping out, then so be it. OCR is such a strange subject, that there's not too much out there that can really really answer the deep-machine ended questions. Especially if you're going to attempt to write your own library. :P
How do you determine which onsets are beats? I am using Spectral Flux for Note Onset Detection and a Running Mean for peak-picking/thresholding.
I am just working with the guitar instrument so the presence of percussions may not help with this. Any ideas?
Thanks!
EDIT: Wow...just realized this question is 3 years old...sorry to resurrect an old post.
My Master's thesis was in beat detection and the main advantage of my method over all other published methods of beat detection was in resolution, both in the time domain and frequency (beat) domain. You can find my thesis here. What it basically boils down to (after alot of filtering) is a comb-filter convolution. My code is an adaptation of this project, which contains Matlab files for you to see how it works.
My code (both in C++ and the Matlab port) is not publicly available due to possible copywrite issues with my university, but if you email me at dberm22[at]gmail[dot]com, I'd be more than willing to ahem::discuss my work with you.
Try using a beat tracking algorithm. Beat tracking is a distinct problem from onset detection.
I think there's a good algorithm in the Queen Mary plugin set for Sonic Visualizer. The plugins are open source, so you can have a look at the code to figure out how they work.
Or do a search on google scholar for "beat tracking". There are a number of effective approaches. Dan Ellis' is a good one to start with. It's intuitive, and there's code available in Matlab and Java.
I am looking for a way to compare a user submitted audio recording against a reference recording for comparison in order to give someone a grade or percentage for language learning.
I realize that this is a very un-scientific way of doing things and is more than a gimmick than anything.
My first thoughts are some sort of audio fingerprinting, or waveform comparison.
Any ideas where I should be looking?
This is by no means a trivial problem to solve, though there is an abundance of research on the topic. Presently the most successful forms of machine learning in the speech recognition domain apply Hidden Markov Model techniques.
You may also want to take a look at existing implementations of HMM algorithms. One such library in its early stages is ghmm.
Perhaps even better and more readily applicable to your problem is HTK.
In addition to chomp's great answer, one important keyword you probably need to look up is Dynamic Time Warping (DTW). This is the wikipedia article: http://en.wikipedia.org/wiki/Dynamic_time_warping