How to Run Sentiment Analysis in Python using VADER

How to Run Sentiment Analysis in Python using VADER

How to Run Sentiment Analysis in Python using VADER. A walk-through example of how you can apply Sentiment Analysis in Thousands of Tweets in a few seconds

Words Sentiment Score

We have explained how to get a sentiment score for words in Python. Instead of building our own lexicon, we can use a pre-trained one like the VADER which stands from _Valence Aware Dictionary and sEntiment Reasoner _and is specifically attuned to sentiments expressed in social media.

You can install the VADER library using pip like pip install vaderSentiment or you can get it directly from NTLK. You can have a look at VADER documentation.

Examples of Sentiment Scores

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 posneu 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

Let’s see these features in practice. We will work with a sample fo twitters obtained from NTLK.

python nltk sentiment-analysis sentiment vader

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How to Calculate a Sentiment Score for Words in Python

For this example, we will use a Twitter dataset that comes with NLTK. This dataset has been manually annotated and serves to establish baselines for models quickly. The sample dataset from NLTK is separated into positive and negative tweets. It contains 5000 positive tweets and 5000 negative tweets exactly. The exact match between these classes is not a coincidence. The intention is to have a balanced dataset. That does not reflect the real distributions of positive and negative classes in live Twitter streams. It is just because balanced datasets simplify the design of most computational methods that are required for sentiment analysis. However, it is better to be aware that this balance of classes is artificial. Let us import them now as well as a few other libraries we will be using. A practical example of how you can Calculate a Sentiment Score for a Token in Python. How to Calculate a Sentiment Score for Words in Python

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