/py-score-emote

A simple python script to parse text into sentences then attach a vader emotion score

Primary LanguagePython

README for Emote-Score Script

Overview

Emote-Score is a Python script designed to analyze the emotional sentiment of text. Utilizing the VADER (Valence Aware Dictionary and sEntiment Reasoner) SentimentIntensityAnalyzer, it breaks down the input text into sentences and calculates sentiment scores for each. The script is ideal for evaluating the emotional tone of any textual content, such as social media posts, reviews, or articles.

Requirements

  • Python 3.x
  • VADERSentiment Python package

To install VADERSentiment, run:

pip install vaderSentiment

Usage

The script can be run from the command line with an input string and an optional output file.

Command Line Syntax

python emote-score.py [-o <output_file>] "<input_string>"
  • -o <output_file>: Optional. Specify the output file to save the results in JSON format. If not provided,
  • output.json will be used.
  • <input_string>: Required. The string of text you want to analyze.

Examples

Analyzing a direct input string:

python emote-score.py -o results.json "This is a sample text to analyze."

Reading input from a file:

cat textfile.txt | python emote-score.py -o results.json

Output

The script outputs a JSON file containing the analyzed sentences along with their VADER sentiment scores. The sentiment scores include compound, neg, neu, and pos values indicating the overall sentiment and the mix of negative, neutral, and positive tones.

Functionality

The Emote-Score script is designed with the following functionalities:

Sentence Splitting: The script processes the input text and splits it into individual sentences based on punctuation marks (periods, exclamation marks, and question marks). This feature ensures that the sentiment analysis is done on a per-sentence basis, providing a more granular understanding of the text's emotional tone.

Sentiment Analysis: Each sentence is analyzed using the VADER SentimentIntensityAnalyzer. This tool is specifically attuned to sentiments expressed in social media and can handle a variety of textual formats. It provides a composite score that quantifies the overall sentiment of the sentence on a scale from highly negative to highly positive.

Output Generation: The results of the sentiment analysis are compiled into a structured JSON format. Each sentence is listed with its corresponding sentiment scores, including the composite score as well as separate scores for negative, neutral, and positive sentiment.

Flexible Input Handling: The script can accept input text either directly through the command line or piped from another command. This flexibility allows for easy integration with other tools and workflows.

Customizable Output: Users have the option to specify the output file's name and location, allowing for better organization of the results.

This functionality makes Emote-Score an effective tool for analyzing the sentiment of various types of text, especially useful in fields like social media analysis, market research, and customer feedback assessment.