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
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.
The VADER library returns 4 values such as:
Notice that the
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:
Let’s see these features in practice. We will work with a sample fo twitters obtained from NTLK.
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
Sentiment analysis is very important to know for businesses this days. The easiest way to conduct sentiment analysis is from text or review.
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