Recently WHO has declared Omicron as a varient of Concern on their Twitter account, so this project passes a light on how people are reacting to this post. This analysis will show whether they are reacting in negative or in positive way or in neutral way.
Dataset
Data are collected from Kaggle, which is a tweet that people have made after WHOtweet the post.
Data Preprocessing and Cleaning
First we clean a null value by dropping it since there is large amount of data.
After cleaning text data using NLTK module and regular expression and by importing Stopwords, wordcloud looks like:
What is used to Calculate Sentiment Score -->> VADER Lexicon
VADER ( Valence Aware Dictionary for Sentiment Reasoning) is a model used for text sentiment analysis that is sensitive to both polarity (positive/negative) and intensity (strength) of emotion. It is available in the NLTK package and can be applied directly to unlabeled text data. VADER sentimental analysis relies on a dictionary that maps lexical features to emotion intensities known as sentiment scores. The sentiment score of a text can be obtained by summing up the intensity of each word in the text. For example- Words like 'love', 'enjoy', 'happy', 'like' all convey a positive sentiment. Also VADER is intelligent enough to understand the basic context of these words, such as "did not love" as a negative statement. It also understands the emphasis of capitalization and punctuation, such as "ENJOY".
What SentimentIntensityAnalyzer do?
Give a sentiment intensity score to sentences.
What polarity_score do?
Return a float for sentiment strength based on the input text. Positive values are positive valence, negative value are negative valence.
Result
Here we find majority of people are neutral, that's amazing thing why? In such a critical post people are not reacting but they are sharing information.
