Using sentiment analysis API and want to know how the AI bias that gets in through the training set of data and other biases quantified. Any help would be appreciated.
There are several tools developed to deal with it:
Fair Learn https://fairlearn.github.io/
Interpretability Toolkit https://learn.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability
In Fair Learn you can see how biased a ML model is after it has been trained with the data set and choose a maybe less accurate model which performs better with biases. The explainable ML models provide different correlation of inputs with outputs and combined with Fair Learn can give an idea of the health of the ML model.
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We are trying to understand the underlying model of Rasa - the forums there still didnt get us an answer - on two main questions:
we understand that Rasa model is a transformer-based architecture. Was it
pre-trained on any data set? (eg wikipedia, etc)
then, if we
understand correctly, the intent classification is a fine tuning task
on top of that transformer. How come it works with such small
training sets?
appreciate any insights!
thanks
Lior
the transformer model is not pre-trained on any dataset. We use quite a shallow stack of transformer which is not as data hungry as deeper stacks of transformers used in large pre-trained language models.
Having said that, there isn't an exact number of data points that will be sufficient for training your assistant as it varies by the domain and your problem. Usually a good estimate is 30-40 examples per intent.
I recently finished a machine learning course and would like to make a forum sentiment analysis tool, to apply it in stock-related forums.
The idea is to:
Capture (text mining) users with their comments, and evaluate their comment's sentiment (positive, negative, neutral).
Capture what happens (stock market) after those comments, and assign a weight to the user accordingly (bigger weight if the user's sentiments is spot-on and the market follows the same direction)
Use the comments as a tool to predict market direction.
Actually, I do this myself (pay attention on forums) plus my own technical analysis and the obligatory due diligence, and it has been working very well for me. I just wanted to try to automate it a little bit and maybe even allow a program to play with some of my accounts (paper trading first, and if it performs decently assign some money in a real account)
This would be my first machine learning project (just as a proof-of-concept) so any comments would be very kindly appreciated.
The biggest problem that I find is that I would like to make an unsupervised training, and I need a sample dataset to do the training.
Question: Is there any known forum-sentiment dataset available to be used for unsupervised training?
I've found several sentiment datasets (twitter, imbd, amazon reviews) but they are very specific to their niche (short messages, movies, products...) but I'm looking for something more general.
Since you are looking for an unsupervised approach you can use any set of data that matches your "real case scenario". Text mining and sentiment analysis are are often tailored to the problem at hand so it is easy to start directly with the real data. The best approach is to built a scraper that grabs directly the forum posts that you want to analyze. You can build the scraper easily enough with Python (beautifulsoup/selenium). Online is full of nice tutorial eg: https://www.dataquest.io/blog/web-scraping-tutorial-python/
We are looking at building recommender system for our brand-new Learning Management System. There are a bunch of users and items (learning modules) onboarded, but no ratings yet - typical cold start problem.
To begin with, we are thinking of using a simple item-based similarity using item attributes (tags, category, etc.) The idea is to switch to more robust collaborative filtering as the ratings start coming in.
Questions:
Is this a good approach? Is there a recommended ML pattern to handle such cold-start conditions?
To realise item-based similarity, which is the right algorithm? Say, cosine similarity. However, please note there is no "matrix". Should we try to use a standard ML algorithm or maybe roll our own?
Your approach is good. I would start with an unsupervised learning algorithm such as 'k-Nearest Neighbors classifier'. If your team doesn't know the first thing about ML, I recommend you to read this tutorial http://www.astroml.org/sklearn_tutorial/general_concepts.html . It uses python and a great library called scikit-learn. From there you could do Andrew's NG course (https://www.coursera.org/learn/machine-learning/) although it does not cover any recommendation systems.
I usually go with a Pearson Correlation algorithm (https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient) and that suffices me for my problems. The problem with this approach is that it is linear. I have read that the Orange data mining tool provides many correlation measures. Using it you could find which one is best for your data. I would advice against using your own algorithm.
There is an older question which provides further information on the matter: How can I implement a recommendation engine?
I'm studying Reinforcement Learning and reading Sutton's book for a university course. Beside the classic PD, MC, TD and Q-Learning algorithms, I'm reading about policy gradient methods and genetic algorithms for the resolution of decision problems.
I have never had experience before in this topic and I'm having problems understanding when a technique should be preferred over another. I have a few ideas, but I'm not sure about them. Can someone briefly explain or tell me a source where I can find something about typical situation where a certain methods should be used? As far as I understand:
Dynamic Programming and Linear Programming should be used only when the MDP has few actions and states and the model is known, since it's very expensive. But when DP is better than LP?
Monte Carlo methods are used when I don't have the model of the problem but I can generate samples. It does not have bias but has high variance.
Temporal Difference methods should be used when MC methods need too many samples to have low variance. But when should I use TD and when Q-Learning?
Policy Gradient and Genetic algorithms are good for continuous MDPs. But when one is better than the other?
More precisely, I think that to choose a learning methods a programmer should ask himlself the following questions:
does the agent learn online or offline?
can we separate exploring and exploiting phases?
can we perform enough exploration?
is the horizon of the MDP finite or infinite?
are states and actions continuous?
But I don't know how these details of the problem affect the choice of a learning method.
I hope that some programmer has already had some experience about RL methods and can help me to better understand their applications.
Briefly:
does the agent learn online or offline? helps you to decide either using on-line or off-line algorithms. (e.g. on-line: SARSA, off-line: Q-learning). On-line methods have more limitations and need more attention to pay.
can we separate exploring and exploiting phases? These two phase are normally in a balance. For example in epsilon-greedy action selection, you use an (epsilon) probability for exploiting and (1-epsilon) probability for exploring. You can separate these two and ask the algorithm just explore first (e.g. choosing random actions) and then exploit. But this situation is possible when you are learning off-line and probably using a model for the dynamics of the system. And it normally means collecting a lot of sample data in advance.
can we perform enough exploration? The level of exploration can be decided depending on the definition of the problem. For example, if you have a simulation model of the problem in memory, then you can explore as you want. But real exploring is limited to amount of resources you have. (e.g. energy, time, ...)
are states and actions continuous? Considering this assumption helps to choose the right approach (algorithm). There are both discrete and continuous algorithms developed for RL. Some of "continuous" algorithms internally discretize the state or action spaces.
I'd like you to give me some advice in order to tackle this problem. At college I've been solving opinion mining tasks but with Twitter the approach is quite different. For example, I used an ensemble learning approach to classify users opinions about a certain Hotel in Spain. Of course, I was given a training set with positive and negative opinions and then I tested with the test set. But now, with twitter, I've found this kind of categorization very difficult.
Do I need to have a training set? and if the answer to this question is positive, don't you think twitter is so temporal so if I have that set, my performance on future topics will be very poor?
I was thinking in getting a dictionary (mainly adjectives) and cross my tweets with it and obtain a term-document matrix but I have no class assigned to any twitter. Also, positive adjectives and negative adjectives could vary depending on the topic and time. So, how to deal with this?
How to deal with the problem of languages? For instance, I'd like to study tweets written in English and those in Spanish, but separately.
Which programming languages do you suggest to do something like this? I've been trying with R packages like tm, twitteR.
Sure, I think the way sentiment is used will stay constant for a few months. worst case you relabel and retrain. Unsupervised learning has a shitty track record for industrial applications in my experience.
You'll need some emotion/adj dictionary for sentiment stuff- there are some datasets out there but I forget where they are. I may have answered previous questions with better info.
Just do English tweets, it's fairly easy to build a language classifier, but you want to start small, so take it easy on yourself
Python (NLTK) if you want to do it easily in a small amount of code. Java has good NLP stuff, but Python and it's libraries are way more user friendly
This site: https://sites.google.com/site/miningtwitter/questions/sentiment provides 3 ways to do sentiment analysis using R.
The twitter package is now updated to work with the new twitter API. I'd you download the source version of the package to avoid getting duplicated tweets.
I'm working on a spanish dictionary for opinion mining, and would publish somewhere accesible.
cheers!
Sentiment Analysis will give only 3 results as said above - positive, negative and neutral. I found a tutorial on Twitter Sentiment analysis and it's quiet easy.
I found it here - https://www.ai-ml.tech/twitter-sentiment-analysis/
Only 3 dependencies, i downloaded and lesser code, done. Just go through it, you will get the solution.