Excerpt
What is VADER?
VADER is a less resource-consuming sentiment analysis model that uses a set of rules to specify a mathematical model without explicitly coding it. VADER consumes fewer resources as compared to Machine Learning models as there is no need for vast amounts of training data. VADER’s resource-efficient approach helps us to decode and quantify the emotions contained in streaming media such as text, audio or video. VADER doesn’t suffer severely from a speed-performance tradeoff.
VADER stands for Valence Aware Dictionary for sEntiment Reasoning.
Python implementation of VADER – Environment Setup
Standard Python distribution doesn’t come bundled with the VADER module. We’ll be using the popular Python package installer, pip to do so.
A package contains all the files you need for a module. Modules are Python code libraries you can include in your project. We use the following code in Anaconda terminal to install VADER.
!pip install vaderSentiment
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()
VADER has been included in the NLTK package itself. Module NLTK is used for natural language processing. NLTK is an acronym for Natural Language Toolkit and is one of the leading platforms for working with human language data. Alternatively one may use.
!pip install nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()
Using Pandas Datareader to scrape stock data
import pandas as pd
import numpy as np
import pandas_datareader as pdr
import matplotlib.pyplot as plt
data_amd = pdr.get_data_yahoo(‘AMD’, ’24-Feb-20′)
data_amd.head()
Visit QuantInsti Blog to read the rest of the article and download the Python code:
https://blog.quantinsti.com/vader-sentiment/
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