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https://github.com/talaikis/stocktalk3
Data collection toolkit for social media analytics ported to Python 3.
https://github.com/talaikis/stocktalk3
data-mining sentiment-analysis twitter
Last synced: about 2 months ago
JSON representation
Data collection toolkit for social media analytics ported to Python 3.
- Host: GitHub
- URL: https://github.com/talaikis/stocktalk3
- Owner: talaikis
- License: mit
- Created: 2017-03-05T09:05:00.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2017-06-03T09:49:36.000Z (over 7 years ago)
- Last Synced: 2024-09-16T13:44:15.773Z (4 months ago)
- Topics: data-mining, sentiment-analysis, twitter
- Language: Python
- Size: 44.9 KB
- Stars: 12
- Watchers: 4
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://pypi.python.org/pypi/stocktalk3)
[](https://travis-ci.org/xenu256/StockTalk3)
# StockTalk3
Data collection toolkit (https://github.com/anfederico/Stocktalk) for social media analytics ported to Python 3.
## Code Examples
#### Install & dependencies
```text
pip install stocktalk3
pip install -r requirements.txt
python -m nltk.downloader vader_lexicon
```
#### Mining Twitter
```python
from stocktalk3 import TwitterAxe
#credentials
CONSUMER_KEY = ""
CONSUMER_SECRET = ""
ACCESS_TOKEN = ""
ACCESS_SECRET = ""
axe = TwitterAxe(api=twitter.Api(consumer_key=CONSUMER_KEY,
consumer_secret=CONSUMER_SECRET,
access_token_key=ACCESS_TOKEN,
access_token_secret=ACCESS_SECRET))
#mining settings
minePeriod = 60*5 # Mine Twitter for 5 minutes
requestFrequency = 30*1 # Request tweets every 30 seconds
analyzeFrequency = 60*1 # Analyze tweets every 1 minute
requestAmount = 100 # With each request pull 100 tweets (max = 100)
similarityCutoff = 90 # Filter out tweets with 90% similarity (default = 90)
# start mining
axe.mine("Apple", minePeriod, requestFrequency, analyzeFrequency, requestAmount, similarityCutoff)
```
```text
Mine complete from
[2016/10/14 02:37:16 PM] - [2016/10/14 02:42:26 PM]
```
#### Save & Explore Data
```python
# show in terminal
axe.showInventory()
axe.showUniqueTweets()
axe.showFilteredTweets()
axe.showTimeSeries()
# save to outputs directory
axe.exportUniqueTweets()
axe.exportFilteredTweets()
axe.exportTimeSeries()
```
```text
# terminal output
Inventory
Unique Tweets: 398
Filtered Tweets: 73
Unique Tweets
DATE_TIME TEXT
0 2017-03-05 09:49:19 @BenPhillipsUK you should pee into a bottle an...
1 2017-03-05 09:49:18 RT @SonyMusicGlobal: #ZAYNmoji is here. https:...
2 2017-03-05 09:49:18 RT @LittleMix: #NoMoreSadSongs feat @machinegu...
... ...
395 2017-03-05 09:53:12 @Apple @AppleSupport than ‘oh, sorry, just wai...
396 2017-03-05 09:53:11 Carol Dysart reveals how to understand ideal c...
397 2017-03-05 09:53:10 R Shawn McBride reveals expert business tips &...
[398 rows x 2 columns]
Filtered Tweets
DATE_TIME TEXT
0 2017-03-05 09:49:10 RT @MayWardFlyersPH: Titig ng Pag-ibig Single ...
1 2017-03-05 09:49:05 RT @MayWardFlyersPH: Titig ng Pag-ibig Single ...
2 2017-03-05 09:48:58 Lazy Sunday morning with some #comfortfood 🍎🌸a...
... ...
70 2017-03-05 09:51:33 RT @uperdeepes: Now on #Itunes go support @iam...
71 2017-03-05 09:53:28 RT @MayWardFlyersPH: Titig ng Pag-ibig Single ...
72 2017-03-05 09:53:17 RT @MayWardFlyersPH: Titig ng Pag-ibig Single ...
[73 rows x 2 columns]
Time Series
TIME SENTIMENT TWEETS
0 2017-03-05 11:50:06 0.354 67
1 2017-03-05 11:51:06 0.202 74
2 2017-03-05 11:52:07 0.279 85
3 2017-03-05 11:53:24 0.342 149
```
#### Visualize Sentiment Analysis
```python
# show plot
axe.showPlot()
# save plot to outputs directory
axe.savePlot(name='apple' [, width=6, height=4.5])
```
## Underlying Features
#### Spam Filtering
```python
from stocktalk3.TwitterAxe import similarityScore
stringOne = "Lawsuits are piling up against Tesla Motors (TSLA) over the Solar City (SCTY) merger"
stringTwo = "Lawsuits piling up against Tesla Motors (TSLA) with the Solar City (SCTY) merger"
print(similarityScore(s1=stringOne, s2=stringTwo))
# 95.12%
stringTwo = "Lawsuits piling up against Tesla Motors (TSLA) with the Solar City (SCTY) merger"
stringThree = "Bad news for Tesla Motors (TSLA) over the Solar City (SCTY) merger"
print(similarityScore(s1=stringTwo, s2=stringThree))
# 82.19%
stringThree = "Bad news for Tesla Motors (TSLA) over the Solar City (SCTY) merger"
stringFour = "Tesla Motors (TSLA) will not need to raise equity or debt before the end of the year"
print similarityScore(stringThree, stringFour)
# 59.33%
```
#### Text Processing
```python
from stocktalk3.TwitterAxe import filter_tweets
textOne = "@TeslaMotors shares jump as shipments more than double! #winning"
print(filter_tweets(text=textOne))
# shares jump as shipments more than double winning
textTwo = "Tesla announces its best sales quarter: http://trib.al/RbTxvSu $TSLA"
print(filter_tweets(text=textTwo))
# tesla announces its best sales quarter
textThree = "Tesla $TSLA reports deliveries of 24500, above most views."
print(filter_tweets(text=textThree))
# tesla reports deliveries of number above most views
```
#### Sentiment Analysis
```python
from stocktalk3.TwitterAxe import sentimentScore
textOne = "shares jump as shipments more than double winning"
print(sentimentScore(texts=[textOne]))
# 0.706
textTwo = "tesla reports deliveries of number above most views"
print(sentimentScore(texts=[textTwo]))
# 0.077
textThree = "not looking good for tesla competition on the rise"
print(sentimentScore(texts=[textThree]))
# -0.341
```