I'm trying to develop something that extract keywords from a text. I know AlchemyAPI work best for this. Now i wanna know what algorithms AlchemyAPI used so that i can implement code of it on my own. Does anyone has any idea about it. Please share it. Thanks in advance.
I have no idea what specific algorithms AlchemyAPI uses (I'm guessing it is on the extreme end of proprietary), but the Stanford NLP has a lot of information and code that may be useful:
http://www-nlp.stanford.edu/software/lex-parser.shtml
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I have a question where I don´t know how to start this. We did a scRNA sequencing experiment and I now have an AnnData dataset. I already know a lot about this dataset mainly by using scanpy library and I would like to "finalise" the analysis by extracting genes that have a specific RNA sequence in the 3'UTR.
Unfortunately I have no idea how to approach this since I am no bioinformatician and couldn´t find a tutorial to do this. Can someone please help me with this problem?
Here how it looks like.
Thanks in advance!
I just found a way around in R. It is on the bases on BSgenome.Mmusculus.UCSC.mm10. You can find the answer here. Just read in the result in python as a list and us the function sc.tl.score_genes to see where they are enriched.
I am pretty new to coding and I'm currently stuck on the following practice questions in my textbook.
Write a program that generates an 'eight puzzle'. It should randomly shuffle the puzzle, then allow the user to solve it.
Extend your program so it has a 'solve' option that will solve it using A* search.
The problem is, after hours of browsing the web and YouTube, I only come across tutorials and examples which either assume advanced knowledge of this topic or include no useful annotations which would help me learn it.
I was wondering if anyone could please point me to a resource of some sort that explains how to even approach such a puzzle in Python 3. I have no clue what the best way to learn and start this is.
Thank you in advance for your time and help.
I am looking for a tool that can analyze the emotion of short texts. I searched for a week and I couldn't find a good one that is publicly available. The ideal tool is one that takes a short text as input and guesses the emotion. It is preferably a standalone application or library.
I don't need tools that is trained by texts. And although similar questions are asked before no satisfactory answers are got.
I searched the Internet and read some papers but I can't find a good tool I want. Currently I found SentiStrength, but the accuracy is not good. I am using emotional dictionaries right now. I felt that some syntax parsing may be necessary but it's too complex for me to build one. Furthermore, it's researched by some people and I don't want to reinvent the wheels. Does anyone know such publicly/research available software? I need a tool that doesn't need training before using.
Thanks in advance.
I think that you will not find a more accurate program than SentiStrength (or SoCal) for this task - other than machine learning methods in a specific narrow domain. If you have a lot (>1000) of hand-coded data for a specific domain then you might like to try a generic machine learning approach based on your data. If not, then I would stop looking for anything better ;)
Identifying entities and extracting precise information from short texts, let alone sentiment, is a very challenging problem specially with short text because of lack of context. Hovewer, there are few unsupervised approaches to extracting sentiments from texts mainly proposed by Turney (2000). Look at that and may be you can adopt the method of extracting sentiments based on adjectives in the short text for your use-case. It is hovewer important to note that this might require you to efficiently POSTag your short text accordingly.
Maybe EmoLib could be of help.
One simple question (but I haven't quite found an obvious answer in the NLP stuff I've been reading, which I'm very new to):
I want to classify emails with a probability along certain dimensions of mood. Is there an NLP package out there specifically dealing with this? Is there an obvious starting point in the literature I start reading at?
For example, if I got a short email something like "Hi, I'm not very impressed with your last email - you said the order amount would only be $15.95! Regards, Tom" then it might get 8/10 for Frustration and 0/10 for Happiness.
The actual list of moods isn't so important, but a short list of generally positive vs generally negative moods would be useful.
Thanks in advance!
--Trindaz on Fedang #NLP
You can do this with a number of different NLP tools, but nothing to my knowledge comes with it ready out of the box. Perhaps the easiest place to start would be with LingPipe (java), and you can use their very good sentiment analysis tutorial. You could also use NLTK if python is more your bent. There are some good blog posts over at Streamhacker that describe how you would use Naive Bayes to implement that.
Check out AlchemyAPI for sentiment analysis tools and scikit-learn or any other open machine learning library for the classifier.
if you have not decided to code the implementation, you can also have the data classified by some other tool. google prediction api may be an alternative.
Either way, you will need some labeled data and do the preprocessing. But if you use a tool that may help you get better accuracy easily.
I am interested to do automatic tagging for bodies of text. I am pretty new to NLP so I would like to hear some methods which you guys are familiar with in this context.
Any recommendations will be appreciated.
bag-of-words approaches should work okay. you're just trying place one tag on the entire text, right?
however, as another member pointed out to me one time, StackOverflow is mostly a programming Q&A.