pysententia

Sententia provides access to media sentiment data from the Bureau for Economic research.

This homepage for the {sententia} python package is at https://github.com/71point4/pysententia.

You can find the homepage for the {sententia} R package is at https://github.com/71point4/sententia.

Installation

Install from GitHub

Install from GitHub.

install git+https://github.com/71point4/pysententia

Documentation

To build the documentation:

make -C docs/ html

Set the API Key

To access the API you’ll need to first specify an API key as provided to you by the BER.

import os

key = os.getenv('SENTENTIA_KEY')

The API interface

Besides providing sentiment calculations from different word-list dictionaries, the API interface provides access to the various permutations that is available in calculating a sentiment score (Within text and across time):

• Sentiment calculation WITHIN the article
  • In the API this is set by the aggr parameter
• Sentiment calculation ACCROSS a time period
  • This does not need to be set. API returns four aggregations:
    • mean_sentiment
    • relative
    • absolute
    • sent_log

Usage

from pysententia import Sententia

sent = Sententia(key = key)

Sentiment Index

Get media sentiment index values for specified media source, model, topic, dictionary, frequency, and aggregation method combination.

sent.sent_index(
   source = "businessday",
   topic = "global",
   freq = "month",
   dict = "loughran",
   aggr = "sent_logit"
   )

Count of articles that make up sentiment

Get a count of the number of articles for a specified media source, model, topic, and frequency of aggregation.

sent.sent_counts(
   source = "all",
   topic = "global",
   freq = "week"
   )

Date polarity

Get a count of the number of positive and negative articles for a specified model, topic, dictionary, aggregation method, and frequency.

sent.sent_date_polarity(
   source = "all",
   topic = "global",
   freq = "week",
   dict = "loughran",
   aggr = "sent_logit"
   )

Word polarity

Get the top 50 most frequently occurring positive and negative words for a specified model, topic, dictionary, aggregation method, and frequency. The timeframe over which these words are selected depends on the specified frequency (day = 30 days, week = 3 months, month = 6 months).

sent.sent_word_polarity(
   source = "all",
   topic = "economy",
   freq = "day",
   dict = "loughran"
   )