webis 0.1.1

Python wrapper for the webis Twitter sentiment identification tool

Python wrapper for the webis Twitter sentiment evaluation ensemble

This is a Python wrapper around the Java implementation of a Twitter sentiment evaluation framework presented by Hagen et al. (2015). The example script fetches Tweets from a PostgreSQL database, uses PyJnius to call the Java modules to evaluate the sentiment, and saves results to a table in the same database.

Dependencies

The script depends on the Python modules PyJnius, pandas and emojientities.

On top of that, a Java Runtime Environment (jre) is required, plus a matching Java Development Kit (jdk). We used Java 8, but other versions might work just as well. OpenJDK works fine.

Installation

• using pip or similar:

pip install webis

• manually:

  • Clone this repository

  git clone https://gitlab.com/christoph.fink/python-webis.git
  

  • Change to the cloned directory
  • Use the Python setuptools to install the package:

  cd python-webis
  python ./setup.py install
  

Usage

First, make sure the environment variable JAVA_HOME is set and pointing to your Java installation.

Then instantiate a webis.SentimentIdentifier object and use its identifySentiment() function, passing in a list of tuples ([(tweetId, tweetText),(tweetId, tweetText), … ]) or a pandas.DataFrame (first column is treated as identifier, second as tweetText).

The function returns a list of tuples ([(tweetId, sentiment), … ]) or a data frame (first column id, second column sentiment) of rows it successfully identified a sentiment of.

import webis

sentimentIdentifier = webis.SentimentIdentifier()

tweets = [
    (1, "What a beautiful morning! There’s nothing better than cycling to work on a sunny day 🚲."),
    (2, "Argh, I hate it when you find seven (7!) cars blocking the bike lane on a five-mile commute")
]

sentimentIdentifier.identifySentiment(tweets)
# [(1, "positive"), (2, "negative")]

import pandas
tweets = pandas.DataFrame(tweets)
sentimentIdentifier.identifySentiment(tweets)
#   sentiment tweetId
# 0  positive       1
# 1  negative       2