I am developing a machine learning project which analyzes requirement specification and categories the non-functional requirements in to categories like database, web socket, backend technology, etc. As I have researched Naive Bayes is the better way to categorize but due to lack of dataset I have planned to go with Seed LDA for topic modeling. Would it be okay to use LDA or should I use something else?
You can try either LDA or clustering.
Based on my experiences, k-mean clustering could help you have a better visualization about what are you doing and what is happening.
With LDA, it could also be good. You can try it first since k-means take much more time.
I implemented an issue tracking system here using k-means, may you like to take a look. issue tracker
Related
Currently, I'm working on a project where I need to extract the relevant aspects used in positive and negative reviews in real time.
For the notions of more negative and positive, it will be a question of contextualizing the word. Distinguish between a word that sounds positive in a negative context (consider irony).
Here is an example:
Very nice welcome!!! We ate very well with traditional dishes as at home, the quality but also the quantity are in appointment!!!*
Positive aspects: welcome, traditional dishes, quality, quantity
Can anyone suggest to me some tutorials, papers or ideas about this topic?
Thank you in advance.
This task is called Aspect Based Sentiment Analysis (ABSA). Most popular is the format and dataset specified in the 2014 Semantic Evaluation Workshop (Task 5) and its updated versions in the following years.
Overview of model efficiencies over the years:
https://paperswithcode.com/sota/aspect-based-sentiment-analysis-on-semeval
Good source for ressources and repositories on the topic (some are very advanced but there are some more starter friendly ressources in there too):
https://github.com/ZhengZixiang/ABSAPapers
Just from my general experience in this topic a very powerful starting point that doesn't require advanced knowledge in machine learning model design is to prepare a Dataset (such as the one provided for the SemEval2014 Task) that is in a Token Classification Format and use it to fine-tune a pretrained transformer model such as BERT, RoBERTa or similar. Check out any tutorial on how to do fine-tuning on a token classification model like this one in huggingface. They usually use the popular task of Named Entity Recognition (NER) as the example task but for the ABSA-Task you basically do the same thing but with other labels and a different dataset.
Obviously an even easier approach would be to take more rule-based approaches or combine a rule-based approach with a trained sentiment analysis model/negation detection etc., but I think generally with a rule-based approach you can expect a much inferior performance compared to using state-of-the-art models as transformers.
If you want to go even more advanced than just fine-tuning the pretrained transformer models then check out the second and third link I provided and look at some of the machine learning model designs specifically designed for Aspect Based Sentiment Analysis.
I understand it’s usually difficult to evaluate unsupervised models. However, since I’m new to NLP, I figured I’d ask to see if there’s a way to compare 2 topic models. I have an STM and an LDA models, but if someone asks how do I choose one vs the other, I have no answer. Is there a way where given the data I have, I can show some metric that I can compare between models and state with some degree of confidence the LDA is better or worse than STM?
Gensim, for Python, has a tutorial showing how to compare LDA models. Since (I believe) the outputs of STM and LDA should be more or less the same, you should be able to do the same analysis.
For what it's worth, I don't know that there is a universally-accepted way to compare models. I've dabbled with it in R doing something similar to the Gensim tutorial. It can be a bit of a headache and (at least in my case) the results weren't super clear.
I want to create a web application which uses machine learning to predict the price of agriculture commodities before 2-3 months.
Is it really feasible or not?
If yes, then please provide some rough idea about which tools and technologies I can use to implement it.
First of all, study math, more precisely, statistics and differential algebra.
Then, use any open (or not) source neural networking libraries you could find. Even MATLAB would help, as it has a good set of examples (I think it has some of alike prediction models, at least I remember creating a model for predicting election results in Poland)
Decide on your training and input data. Research how news and global situation influences commodity prices. Research how existing bots predict prices for next 1-2 minutes. Also consider using history of predictions from certain individuals, I think Reuters has some API for this. Saying this I imply you'll have to integrate natural language processors, too.
Train your model, test it, improve it for quite a long time.
Finally, deploy a boring front-end and monetize it.
If you dont want to implement ML, you can also use kalman filters.
A wanna-be data-scientist here and am trying to understand as a data scientist, when and why would you use a Probability Density Function (PDF)?
Sharing a scenario and a few pointers to learn about this and other such functions like CDF and PMF would be really helpful. Know of any book that talks about these functions from practice stand-point?
Why?
Probability theory is very important for modern data-science and machine-learning applications, because (in a lot of cases) it allows one to "open up a black box" and shed some light into the model's inner workings, and with luck find necessary ingredients to transform a poor model into a great model. Without it, a data scientist's work is very much restricted in what they are able to do.
A PDF is a fundamental building block of the probability theory, absolutely necessary to do any sort of probability reasoning, along with expectation, variance, prior and posterior, and so on.
Some examples here on StackOverflow, from my own experience, where a practical issue boils down to understanding data distribution:
Which loss-function is better than MSE in temperature prediction?
Binary Image Classification with CNN - best practices for choosing “negative” dataset?
How do neural networks account for outliers?
When?
The questions above provide some examples, here're a few more if you're interested, and the list is by no means complete:
What is the 'fundamental' idea of machine learning for estimating parameters?
Role of Bias in Neural Networks
How to find probability distribution and parameters for real data? (Python 3)
I personally try to find probabilistic interpretation whenever possible (choice of loss function, parameters, regularization, architecture, etc), because this way I can move from blind guessing to making reasonable decisions.
Reading
This is very opinion-based, but at least few books are really worth mentioning: The Elements of Statistical Learning, An Introduction to Statistical Learning: with Applications in R or Pattern Recognition and Machine Learning (if your primary interest is machine learning). That's just a start, there are dozens of books on more specific topics, like computer vision, natural language processing and reinforcement learning.
Objective: a node.js function that can be passed a news article (title, text, tags, etc.) and will return a category for that article ("Technology", "Fashion", "Food", etc.)
I'm not picky about exactly what categories are returned, as long as the list of possible results is finite and reasonable (10-50).
There are Web APIs that do this (eg, alchemy), but I'd prefer not to incur the extra cost (both in terms of external HTTP requests and also $$) if possible.
I've had a look at the node module "natural". I'm a bit new to NLP, but it seems like maybe I could achieve this by training a BayesClassifier on a reasonable word list. Does this seem like a good/logical approach? Can you think of anything better?
I don't know if you are still looking for an answer, but let me put my two cents for anyone who happens to come back to this question.
Having worked in NLP i would suggest you look into the following approach to solve the problem.
Don't look for a single package solution. There are great packages out there, no doubt for lots of things. But when it comes to active research areas like NLP, ML and optimization, the tools tend to be atleast 3 or 4 iterations behind whats there is academia.
Coming to the core problem. What you want to achieve is text classification.
The simplest way to achieve this would be an SVM multiclass classifier.
Simplest yes, but also with very very (see the double stress) reasonable classification accuracy, runtime performance and ease of use.
The thing which you would need to work on would be the feature set used to represent your news article/text/tag. You could use a bag of words model. add named entities as additional features. You can use article location/time as features. (though for a simple category classification this might not give you much improvement).
The bottom line is. SVM works great. they have multiple implementations. and during runtime you don't really need much ML machinery.
Feature engineering on the other hand is very task specific. But given some basic set of features and a good labelled data you can train a very decent classifier.
here are some resources for you.
http://svmlight.joachims.org/
SVM multiclass is what you would be interested in.
And here is a tutorial by SVM zen himself!
http://www.cs.cornell.edu/People/tj/publications/joachims_98a.pdf
I don't know about the stability of this but from the code its a binary classifier SVM. which means if you have a known set of tags of size N you want to classify the text into, you will have to train N binary SVM classifiers. One each for the N category tags.
Hope this helps.