Sentiment Analysis using VADER

Introduction

Sentiment Analysis is a supervised Machine Learning technique used to determine if a chunk of text is positive, negative or neutral. In text analytics, natural language processing (NLP) and machine learning (ML) techniques are combined to assign sentiment scores to topics, categories or entities within a phrase.

Sentiment analysis is a powerful tool that businesses can leverage to better understand the overall opinions of their customer, gain insights and make data-driven decisions.

In this tutorial we will classify text articles using VADER on a dataset from CrowdFlower.

Import Libraries

import pandas as pd
import nltk
from nltk.corpus import stopwords
from nltk.probability import FreqDist
from nltk.tokenize import sent_tokenize, RegexpTokenizer
from nltk.sentiment.vader import SentimentIntensityAnalyzer
nltk.download('stopwords')
nltk.download('vader_lexicon')
nltk.download('punkt')

Data Collection

For the sake of this tutorial we’re going to use a simple dataset from CrowdFlower via data.world (IMDB Sentiment Sampled). For more info visit the link:

https://data.world/robbertb/imdb-sentiment-sampled

df = pd.read_csv('imdb_sentiment.csv')
df.head()
review
0Protocol is an implausible movie whose only sa...
1I just watched The Dresser this evening, havin...
2Besides being boring, the scenes were oppressi...
3I'm not sure why the producers needed to trade...
4Honestly - this short film sucks. the dummy us...

Preprocessing

The NLTK module is a massive tool kit, aimed to help you with the entire Natural Language Processing (NLP) methodology. NLTK will aid you with everything from splitting sentences from paragraphs, splitting up words, recognizing the part of speech of those words.

In order to apply polarity score we need to prepare your data, we start by the converting our data to lowercase using the built-in function “lower”.

df['review'] = df['review'].apply(lambda txt: txt.lower())

Removing stop word using NLTK by downloading the english stopwords.

stop_words=stopwords.words('english')
df['review'] = df['review'].apply(lambda txt: ' '.join([word for word in txt.split() if word not in stop_words]))

Sentences Tokenization

df['review'] = df['review'].apply(lambda txt: sent_tokenize(txt))

Join the tokenized data into text.

df['review'] = df['review'].apply(lambda txt: ' '.join(txt))

VADER

VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media.

It is used for sentiment analysis of text which has both the polarities i.e. positive/negative. VADER is also used to quantify how much positive or negative emotion the text has and also the intensity of emotion.

Polarity classification

The VADER library returns 4 values such as:

  • pos: The probability of the sentiment to be positive
  • neu: The probability of the sentiment to be neutral
  • neg: The probability of the sentiment to be negative
  • compound: The normalized compound score which calculates the sum of all lexicon ratings and takes values from -1 to 1

Notice that the pos, neu and neg probabilities add up to 1. Also, the compound score is a very useful metric in case we want a single measure of sentiment. Typical threshold values are the following:

  • positive: compound score>=0.05
  • neutral: compound score between -0.05 and 0.05
  • negative: compound score<=-0.05

Instantiate a new object with NLTK SentimentIntensityAnalyzer

sid = SentimentIntensityAnalyzer()

Now we can create a new column to the original DataFrame to store the polarity_scores dictionary, the scores extracted will have the keys “neg”, “neu”, “pos”, “compound” derived from the composite score.

df['score'] = df['review'].apply(lambda txt: sid.polarity_scores(txt))
df['negative'] = df['score'].apply(lambda txt: txt['neg'])df['neutral'] = df['score'].apply(lambda txt: txt['neu'])df['positive'] = df['score'].apply(lambda txt: txt['pos'])df['compound'] = df['score'].apply(lambda txt: txt['compound'])

We will follow by creating a function called “polarity_score” to calculate the accuracy test for each review in our dataframe. We can finally apply the function by creating a new column called “sentiment”.

The reviews in this column will be classified into positive, negative and neutral.

def polarity_score(compound):
if compound > 0.05:
return "positive"
elif compound < -0.5:
return "negative"
elif compound >= -0.05 and compound < 0.05:
return "neutral"
df['sentiment'] = df['compound'].apply(lambda val: polarity_score(val))
df.head()
df['sentiment'].value_counts()

Conclusion

VADER classifies the sentiments very well. It is easy to use, the ready-made model which can be used across multiple domains, social-media texts.

Thanks for reading and happy web scraping everyone!

You can find my Jupyter Notebook for this on my Github.

--

--

--

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

An Algorithm Enforced Database for Unpublished Studies

Colormap Design

Build a Classification Model in BigQuery — Machine learning for Ecommerce

Moving Forward with Brian Freitag

DSL Final Project — Spring 2021

Data Science Internship Experience at LGM

What is Data Science and its Necessity || DS Roadmap

Predicting Churned Customers for music streaming platform Sparkify

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Boula Akladyous

Boula Akladyous

More from Medium

The Theory Behind Naive Bayes | NLP Python Classification Example

Quick Start : building Sentiment Analysis Models

A comparison of sentiment analysis techniques targeting cheap POC deployment on Azure ML

Classify text by language in a column of DataFrame with Python. NLP process.