What is the VADER model in sentiment analysis?

Sentiment analysis plays a pivotal role in natural language processing (NLP) by enabling the automated assessment of the sentiment or emotional tone conveyed within a given text. This analytical process finds extensive utility in tasks such as comprehending customer feedback, monitoring social media trends, and assessing public sentiment.

We’ll discuss the VADER model, a widely recognized and user-friendly tool used for sentiment analysis.

What is VADER?

VADER stands for Valence Aware Dictionary for sEntiment Reasoning. It’s a pre-built sentiment analysis model for social media text, product reviews, and other short text passages. VADER is part of the Natural Language Toolkit (NLTK) library in Python, making it accessible and easy to use.

To install VADER, use the following command:

The import statement to use VADER sentiment in the application is:

Let’s see how VADER works in the following section.

How does VADER work?

VADER works in the following way:

• Lexicon-based approach: VADER relies on a predefined lexicon (dictionary) containing words and phrases, each assigned a sentiment score.

• Tokenization: The input text is divided into individual words or phrases, a process known as tokenization.

• Data handling: We clean and handle specific cases appropriately after breaking the text into tokens.

  • Sentiment scores calculation: Each token is examined and checked against the lexicon for sentiment scores associated with each token. The lexicon encompasses both single words and phrases to capture contextual sentiment.

  • Handling capitalization and punctuation: VADER considers the influence of capitalization and punctuation on sentiment. For instance, it recognizes that “GREAT!” is more positive than “great.”

  • Handling of negations: VADER identifies negations within the text. It comprehends that terms like “not good” convey negative sentiment.

  • Incorporating intensifiers and modifiers: VADER considers intensifying words like “very” or “extremely” and recognizes that modifiers like “very good” are more positive than “good.”

• Sentiment aggregation: VADER calculates sentiment polarity scores for individual tokens. These scores are combined to determine an overall sentiment score for the entire text.

• Classification into sentiment categories: Based on the overall sentiment score, VADER classifies the text into one of three categories: positive, negative, or neutral.

Code example

Let’s look at a code example about how to calculate the polarity scores and sentiment of a text using the VADER model:

Code explanation

Let’s discuss the above code in detail.

• Line 1: We import the SentimentIntensityAnalyzer class from the vaderSentiment library.

• Line 3: We define a function sentiment that takes a single argument sentence representing the text for which we want to analyze the sentiment.

• Line 4: We create an instance of SentimentIntensityAnalyzer and assign it to the variable vader.

• Line 5: We use the polarity_scores method of the vader object to analyze the sentiment of the sentence input. The result is stored in the sentiment dictionary containing positive, neutral, negative, and compound scores.

• Lines 7–9: We calculate overall_sentiment of sentence and return it by calculating the polarity. The overall sentiment is based on the compound score in the sentiment dictionary. If the compound score is greater than or equal to $0.05$, it’s considered positive. It’s considered negative if it’s less than or equal to $-0.05$. Otherwise, it’s considered neutral.

• Lines 12–16: We define a sentences list for which sentiment analysis will be performed.

• Lines 18–21: We iterate through each sentence in the sentences list and call the sentiment_scores function for each one. This code performs sentiment analysis on a list of sentences using the VADER model and displays the overall sentiment.

• Line 20: We print “Overall sentiment:” to indicate that the following output will show the overall sentiment of the sentence.

Limitations of VADER

Along with having many benefits in the field of NLP and being the most used pre-built model for sentiment analysis, this model has some limitations. Some of them are listed below:

• Contextual understanding: VADER’s lexicon-based approach might struggle with contextual understanding. It might misinterpret sarcastic or nuanced expressions that depend heavily on the context.

• Subjectivity and cultural bias: Like any lexicon-based approach, VADER is subject to the biases and subjectivity present in the lexicon itself. It might not accurately capture sentiment in languages other than English or culturally specific expressions.

• Handling of mixed sentiments: VADER assigns an overall sentiment label (positive, negative, neutral) based on the compound score. However, it might not effectively handle text with mixed sentiments where both positive and negative sentiments coexist.

• Lack of target identification: VADER doesn’t identify specific targets or entities within the text. For example, it might not distinguish between sentiments expressed about a product and sentiments expressed about customer service in a product review.

• Customization complexity: While VADER allows some customization through threshold adjustments, fine-tuning it for specific domains or languages can be complex and might require manual lexicon modifications.

Conclusion

The VADER model offers a valuable and accessible tool for sentiment analysis, particularly in scenarios involving short and informal text, such as social media content and product reviews. Its strengths lie in its lexicon-based approach, which allows it to handle emoticons, informal language, and context to some extent. VADER’s output provides sentiment polarity (positive, negative, neutral) and an intensity score, which can be informative for understanding sentiment trends.