NLP Series : Sentiment Analysis of Covid-19 Tweets using Python

Sentiment analysis (or opinion mining) is a natural language processing technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.
Sentiment analysis models focus on polarity (positive, negative, neutral) but also on feelings and emotions (angry, happy, sad, etc), urgency (urgent, not urgent) and even intentions (interested v. not interested).
Sentiment analysis is extremely important because it helps businesses quickly understand the overall opinions of their customers. By automatically sorting the sentiment behind reviews, social media conversations, and more, you can make faster and more accurate decisions.

With the current covid-19 pandemic, we see a lot of stuides and research going on in the world today around covid-19. In this article, using data sets of tweets about covid-19 obtained from Kaggle, A text classifier was built to classify sentiments from the tweets. The kaggle data set from curated from tweeter by scraping the tweeter API for all mentions of covid-19.

To build a text classifier, we need to vectorize the text that will be used for training. There are different ways text can be vectorized. Some of the common techniques used are bag of words, Term Frequency-Inverse Document Frequency(TF-IDF), and Word Embeddings- Word2Vec and GLOVE.

In this talk, we will use Bag of Words and TF-IDF to create feature vectors. We will train models using Logistic Regression, Naive Bayes, and Deep Neural Nets.