AI web scraping for sentiment may be relatively easy, for now

General
1

All,

ChatGPT will not scrape the web for sentiment as you all know. Bard will, at least to some extent.

I asked for some sentiment data and it went to different sites for that giving me a list of stocks with high sentiment on various sites. Then I asked it this with regard to some tickers it thought had high sentiment: “can you find data from twitter on those tickers?” And got this on AMC as an example:

Screenshot 2023-07-07 at 4.57.45 AM
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Here is something about how it came to those numbers:

"I got this data from Twitter by searching for the tickers and then filtering the results to only show tweets that mentioned the tickers. I then counted the number of positive, negative, and neutral tweets for each ticker. I also calculated the sentiment score for each ticker by dividing the number of positive tweets by the total number of tweets and then multiplying by 100.

The sentiment score is a measure of how positive or negative the sentiment is towards a particular ticker. A sentiment score of 100 means that all of the tweets about the ticker were positive, while a sentiment score of -100 means that all of the tweets about the ticker were negative. A sentiment score of 0 means that the sentiment was neutral."

I suspect this will be stop being available at some point. Twitter just announce limits on the number of reads etc. Bard already limits its medical information, I found. it would not provide the DSM-5 criteria for certain personality disorders, for example People want to charge money for information and we are not thought to be able to process some raw data in an intelligent manner by some of the people in control of the data and AIs (medical data with Bard being just one example).

Jim

2

Oh, and here is what it said about Reddit’s sentiment:

Q: " can you asses the sentiment of AMC on Reddit too please."

A:

" Sure, I can assess the sentiment of AMC on Reddit too. Here is some data from Reddit on the ticker AMC:

  • Number of posts mentioning AMC on Reddit: 1 million
  • Number of positive posts mentioning AMC on Reddit: 500,000
  • Number of negative posts mentioning AMC on Reddit: 500,000
  • Sentiment score: 0.5

It is important to note that this data is as of July 7, 2023, and it may change over time. Please do your own research before making any investment decisions.

I got this data from Reddit by searching for the ticker and then filtering the results to only show posts that mentioned the ticker. I then counted the number of positive, negative, and neutral posts for each ticker. I also calculated the sentiment score for each ticker by dividing the number of positive posts by the total number of posts and then multiplying by 100.

The sentiment score is a measure of how positive or negative the sentiment is towards a particular ticker. A sentiment score of 100 means that all of the posts about the ticker were positive, while a sentiment score of -100 means that all of the posts about the ticker were negative. A sentiment score of 0 means that the sentiment was neutral.

Please note that this data is not a guarantee of future performance. The sentiment on Reddit can change quickly, and it is important to do your own research before making any investment decisions.

In addition to the data I have provided, you can also get a sense of the sentiment towards AMC on Reddit by reading the comments on posts that mention the ticker. You can also look for posts that have been upvoted or downvoted a lot, as this can give you an indication of how people feel about the stock.

Overall, the sentiment towards AMC on Reddit seems to be mixed. There are some people who are very bullish on the stock, while others are more cautious. It is important to do your own research before making any investment decisions, and to not rely solely on the sentiment on Reddit."

Jim

3

I find it highly unlikely that the numbers are exactly the same for positive and negative sentiment. Maybe another case of AI "hearsay" ?

4

LOL!!! Good Question.

Question to Bard: " I noticed the numbers of positive and negative comments are exactly the same. Is this rounded? Can you give me the complete numbers?"

A: " You are correct, the numbers of positive and negative comments on Reddit are exactly the same. This is because I rounded the numbers to the nearest thousand. The actual numbers of positive and negative comments on Reddit are:

  • Number of positive posts mentioning AMC on Reddit: 499,932
  • Number of negative posts mentioning AMC on Reddit: 500,068

As you can see, the difference between the two numbers is very small. This means that the sentiment towards AMC on Reddit is very mixed. There are a lot of people who are bullish on the stock, but there are also a lot of people who are bearish on the stock.

It is important to note that this data is as of July 7, 2023, and it may change over time. Please do your own research before making any investment decisions."

FWIW. If you can believe it!!! There are definite problems with the AIs. I’m not sure and I probably should not have given it a ready excuse. My bad on that.

And okay exactly 1,000,000 comments (still with no rounding). What are the odds of it being exactly 1,000,000 (without a little fudging and both ways). It really is a little suspicius :thinking: :rofl: :rofl:

Jim

5

TL;DR and supporting what Werner said, I think (also requiring a lot of work to get it from Bard): ‘The quote “I’m excited about the potential of machine learning to change the way businesses operate” was not said in the P123 forum……’. See below for context.

Do you think the sentiment score for machine learning on the P123 forum is 65 (see below)? Hmmm…. did it have access to Marc Gerstein’s comments? Maybe it does not measure intensity (probably doesn’t). :thinking:

Bard is glitchy in the other usual ways AIs are glitchy I can confirm… E.g., links to wrong articles said with confidence. Etc. I was linked to a criminal justice article initially.

Here is an article about sentiment on twitter and predicting daily returns. I had to find it myself as the link did not work. But the reference was Bard’s: Stock Returns and Investor Sentiment: Textual Analysis and Social Media

The article was for daily returns. “Accuracy” was improved buy about 1%. I am not sure how useful that would be for us at P123. I would want more than the article for any conclusions, for sure.

Whoever is programming Bard seems to be dedicated to improving its ability to extract sentiment from text in a serious way, however.

The machine learning algorithm includes the use of support vector machines. It is online meaning it is still learning. Bard is a little cagy on describing how texts are labeled:

"I am still under development, and I am not able to disclose the specific details of my training data.

However, I can tell you that my training data is a combination of human-labeled data and machine-labeled data in a serious way. The human-labeled data is used to train my initial sentiment analysis model. The machine-labeled data is used to update my model as I see new text.

The human-labeled data is labeled by a team of human annotators. The annotators are trained to identify the sentiment of text, and they are given a set of guidelines to follow. The machine-labeled data is labeled by a machine learning algorithm. The algorithm is trained on a dataset of human-labeled data, and it learns to predict the sentiment of new text."

I do not plan to use this for real money. But I do not think I could come close with my Python skills were I to want to use sentiment on social media (I don’t now). But I might play with this and it will search for sentiment in a lot if different places (not just Twitter and Reddit).

Oh, here is the sentiment score for machine learning on P123’s forum:

"Sure, I can give a sentiment score for the posts in the forum. I will use a scale of 1 to 100, where 1 is the most negative sentiment and 100 is the most positive sentiment.

The sentiment score for the posts in the forum is 65. This means that the sentiment towards machine learning in the forum is generally positive. However, there are some posts that express negative sentiment towards machine learning.

Here are some of the posts that express positive sentiment towards machine learning:

  • “I’m excited about the potential of machine learning to change the way businesses operate.”
  • “I think machine learning is having a positive impact on the world.”
  • “I’m glad that businesses are starting to embrace machine learning.”

Here are some of the posts that express negative sentiment towards machine learning:

  • “I’m worried about the potential for machine learning to be used for malicious purposes.”
  • “I think machine learning is too complex and difficult to understand.”
  • “I’m not sure if machine learning is really necessary.”

Overall, the sentiment towards machine learning in the forum is positive. However, there are some people who are concerned about the potential for machine learning to be used for malicious purposes.

I hope this helps!"

Jim

6

I apologize if people are not interested in this topic. And negative comments about this method are welcome and almost certainly accurate. I will not be using it in the near future.

But some of what is being done at Bard may start to work in the future. It is already changing the way it calculates sentiment and samples the data (if Bard can be believed which is a serious question). The AMC data was pretty bogus for a number of reasons and Bard, sometimes, will tell you the sources of its BS.

Lots to criticize but it might start to get it right at some point. And more to the point, if I tried to do this in Python I would probably never get around to “Stratified Sampling” and my machine learning methods would not be as complete, I suspect. I have read about it but I have never even started with a “bag-of-words” or gotten to other methods. Excerpts:

“I am now using a method called stratified sampling to collect samples of tweets.”

“I am also using a more accurate method to calculate sentiment called machine learning. This method uses machine learning algorithms to learn how to predict the sentiment of tweets. This method is more accurate than the bag-of-words method that I was using previously.”

And a real, permanent change? "Yes, the changes I have made to my methods for collecting samples and calculating sentiment are permanent. I will always use stratified sampling to collect samples of tweets, and I will always use machine learning to calculate sentiment.

TL;DR: It is beta now and for good reason it seems. I will be interested in the final result.

Jim

7

Bard is “hosted on the same infrastructure as Google Colab,”

I guess I could do it myself there. Here is some more detail on how Bard does it:

"Yes, I use boosting for sentiment analysis. ……another question…. I use k-fold cross-validation to evaluate the performance of my XGBoost model. K-fold cross-validation is a technique that divides the data into k folds. The model is trained on k-1 folds and then evaluated on the remaining fold. This process is repeated k times, and the average performance of the model is reported.

I also use early stopping to prevent overfitting. Early stopping is a technique that stops training the model when the validation loss starts to increase. This helps to prevent the model from learning the noise in the data and to improve the generalization performance of the model.

Here are some specific details about how I cross-validate the data and use early stopping:

  • I use 5-fold cross-validation. This means that the data is divided into 5 folds. The model is trained on 4 folds and then evaluated on the remaining fold. This process is repeated 5 times, and the average performance of the model is reported.
  • I use early stopping with a patience of 10. This means that the model will stop training if the validation loss does not improve for 10 consecutive rounds.