Algorithmically predict public sentiment on a topic using VADER sentiment analysis
Readme
Algorithmically predict public sentiment on a topic using flair sentiment analysis.
Installation is simple; just install via pip.
$ pip3 install abraham3k
The most simple way of use is to use the _summary functions.
from abraham3k.prophets import Abraham
from datetime import datetime, timedelta
watched = ["amd", "tesla"]
darthvader = Abraham(
news_source="newsapi",
newsapi_key="xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
bearer_token="xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
weights={"desc": 0.33, "text": 0.33, "title": 0.34},
)
scores = darthvader.news_summary(
watched,
start_time=datetime.now() - timedelta(days=1)
end_time=datetime.now(),
)
print(scores)
'''
{'amd': (56.2, 43.8), 'tesla': (40.4, 59.6)} # returns a tuple (positive count : negative count)
'''
scores = darthvader.twitter_summary(
watched,
start_time=datetime.now() - timedelta(days=1)
end_time=datetime.now(),
)
print(scores)
'''
{'amd': (57, 43), 'tesla': (42, 58)} # returns a tuple (positive count : negative count)
'''
You can run the function news_sentiment to get the raw scores for the news. This will return a nested dictionary with keys for each topic.
from abraham3k.prophets import Abraham
from datetime import datetime, timedelta
darthvader = Abraham(news_source="google")
scores = darthvader.news_sentiment(["amd",
"microsoft",
"tesla",
"theranos"],
)
print(scores['tesla']['text'])
'''
desc datetime probability sentiment
0 The latest PassMark ranking show AMD Intel swi... 2021-04-22T18:45:03Z 0.999276 NEGATIVE
1 The X570 chipset AMD offer advanced feature se... 2021-04-22T14:33:07Z 0.999649 POSITIVE
2 Apple released first developer beta macOS 11.4... 2021-04-21T19:10:02Z 0.990774 POSITIVE
3 Prepare terror PC. The release highly anticipa... 2021-04-22T18:00:02Z 0.839055 POSITIVE
4 Stressing ex x86 Canadian AI chip startup Tens... 2021-04-22T13:00:07Z 0.759295 POSITIVE
.. ... ... ... ...
95 Orthopaedic Medical Group Tampa Bay (OMG) exci... 2021-04-21T22:46:00Z 0.979155 POSITIVE
96 OtterBox appointed Leader, proudly 100% Austra... 2021-04-21T23:00:00Z 0.992927 POSITIVE
97 WATG, world's leading global destination hospi... 2021-04-21T22:52:00Z 0.993889 POSITIVE
98 AINQA Health Pte. Ltd. (Headquartered Singapor... 2021-04-22T02:30:00Z 0.641172 POSITIVE
99 Press Release Nokia publish first-quarter repo... 2021-04-22T05:00:00Z 0.894449 NEGATIVE
'''
The same way works for the twitter API (see below for integrating twitter usage).
from abraham3k.prophets import Abraham
from datetime import datetime, timedelta
darthvader = Abraham(news_source="google")
scores = darthvader.twitter_sentiment(["amd",
"microsoft",
"tesla",
"theranos"]
)
You can also just use a one-off function to get the sentiment from both the news and twitter combined.
from abraham3k.prophets import Abraham
from datetime import datetime, timedelta
darthvader = Abraham(news_source="google")
scores = darthvader.summary(["tesla", "amd"], weights={"news": 0.5, "twitter": 0.5})
print(scores)
'''
{'amd': (59.0, 41.0), 'tesla': (46.1, 53.9)}
'''
There's also a built-in function for building a dataset of past sentiments. This follows the same format as the non-interval functions (twitter_summary_interval, news_summary_interval, summary_interval).
from abraham3k.prophets import Abraham
from datetime import datetime, timedelta
# this works best using the offical twitter api rather than twint
darthvader = Abraham(bearer_token="xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")
scores = twitter_summary_interval(
self,
["tesla", "amd"],
oldest=datetime.now() - timedelta(days=1),
newest=datetime.now(),
interval=timedelta(hours=12),
offset=timedelta(hours=1),
size=100,
)
print(scores)
'''
timestamp positive negative lag
0 2021-05-08 11:46:57.033549 61.0 39.0 0 days 12:00:00
1 2021-05-08 10:46:57.033549 54.0 46.0 0 days 12:00:00
2 2021-05-08 09:46:57.033549 68.0 32.0 0 days 12:00:00
3 2021-05-08 08:46:57.033549 78.0 22.0 0 days 12:00:00
4 2021-05-08 07:46:57.033549 71.0 29.0 0 days 12:00:00
5 2021-05-08 06:46:57.033549 74.0 26.0 0 days 12:00:00
6 2021-05-08 05:46:57.033549 63.0 37.0 0 days 12:00:00
7 2021-05-08 04:46:57.033549 74.0 26.0 0 days 12:00:00
8 2021-05-08 03:46:57.033549 53.5 46.5 0 days 12:00:00
9 2021-05-08 02:46:57.033549 51.0 49.0 0 days 12:00:00
10 2021-05-08 01:46:57.033549 61.0 39.0 0 days 12:00:00
11 2021-05-08 00:46:57.033549 46.9 53.1 0 days 12:00:00
12 2021-05-07 23:46:57.033549 54.0 46.0 0 days 12:00:00
13 2021-05-07 22:46:57.033549 52.0 48.0 0 days 12:00:00
14 2021-05-07 21:46:57.033549 58.0 42.0 0 days 12:00:00
15 2021-05-07 20:46:57.033549 46.0 54.0 0 days 12:00:00
16 2021-05-07 19:46:57.033549 40.0 60.0 0 days 12:00:00
17 2021-05-07 18:46:57.033549 40.0 60.0 0 days 12:00:00
18 2021-05-07 17:46:57.033549 51.0 49.0 0 days 12:00:00
19 2021-05-07 16:46:57.033549 21.0 79.0 0 days 12:00:00
20 2021-05-07 15:46:57.033549 52.5 47.5 0 days 12:00:00
21 2021-05-07 14:46:57.033549 36.0 64.0 0 days 12:00:00
22 2021-05-07 13:46:57.033549 42.0 58.0 0 days 12:00:00
23 2021-05-07 12:46:57.033549 40.0 60.0 0 days 12:00:00
24 2021-05-07 11:46:57.033549 32.0 68.0 0 days 12:00:00
'''
Google trends is also in the process of being added. Currently, there's support for interest over time. You can access it like this.
from abraham3k.prophets import Abraham
from datetime import datetime, timedelta
darthvader = Abraham()
results = darthvader.interest_interval(
["BTC USD", "buy bitcoin"],
start_time=(datetime.now() - timedelta(days=52)),
end_time=datetime.now())
print(results)
'''
BTC USD buy bitcoin
date
2021-03-24 62 18
2021-03-25 68 16
2021-03-26 58 12
2021-03-27 47 15
2021-03-28 48 15
...
2021-05-08 48 27
2021-05-09 38 25
2021-05-10 43 20
2021-05-11 44 24
2021-05-12 38 20
'''
Numbers represent search interest relative to the highest point on the chart for the given region and time. A value of 100 is the peak popularity for the term. A value of 50 means that the term is half as popular. A score of 0 means there was not enough data for this term.
Abraham supports two news sources: Google News and NewsAPI. Default is Google News, but you can change it to NewsAPI by passing Abraham(news_source='newsapi', api_key='<your api key') when instantiating. I'd highly recommend using NewsAPI. It's much better than the Google News API. Setup is really simple, just head to the register page and sign up to get your API key.
I'd highly recommend integrating twitter. It's really simple; just head to Twitter Developer to sign up and get your bearer token. If you don't want to sign up, you can actually use it free with the twint API (no keys needed). This is the default.
I've made it pretty simple (at least for me) to push updates. Once I'm in the directory, I can run $ ./build-push 1.2.0 "update install requirements" where 1.2.0 is the version and "update install requirements" is the git commit message. It will update to PyPi and to the github repository.
Currently, there's another algorithm in progress (SALT), including salt.py and salt.ipynb in the abraham3k/ directory and the entire models/ directory. They're not ready for use yet, so don't worry about importing them or anything.
Pull requests welcome!
Coming soon. However, there is heavy documentation in the actual code.
FAQs
Algorithmically predict public sentiment on a topic using VADER sentiment analysis
We found that abraham3k demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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