In this project, I have attempted to analyze the Covid-19 related tweets dataset and build a machine learning model to classify a tweet as positive, negative or neutral.
This project contains an executable iPython Notebook, a presentation and source as follows:
- Sentiment_Analysis_of_Covid_19_related_Tweets.ipynb - Google Colab notebook containing data summary, exploration, visualisations, text processing, modelling and performance evaluation.
- Coronavirus Tweets.csv - Includes Covid-19 related tweets data.
Since the outbreak of coronavirus, it has affected more than 180 countries where massive losses in the economy and jobs globally and confining about 58% of the global population are caused. The research on people’s feelings is essential for keeping mental health and informed about Covid-19. The given challenge is to build a classification model to predict the sentiment of Covid-19 tweets.
- Understanding the business task.
- Reading data from files given.
- Data pre-processing.
- Data visualization.
- Text processing.
- Modelling data.
- Conclusion.
Four different algorithms were tried out to find out which one performs the best.
- Logistic Regression
- Random Forest
- Naive Bayes
- Support Vector Machine
Midhun R | Avid Learner | Data Analyst | Data Scientist | Machine Learning Enthusiast
Contact me for Data Science Project Collaborations
DataRobot, 'Using Machine Learning for Sentiment Analysis: a Deep Dive'. [Online].
Available: https://www.datarobot.com/blog/using-machine-learning-for-sentiment-analysis-a-deep-dive/
Analytics Vidhya, 'Quick Introduction to Bag-of-Words (BoW) and TF-IDF for Creating Features from Text'. [Online].
Available: https://www.analyticsvidhya.com/blog/2020/02/quick-introduction-bag-of-words-bow-tf-idf/
Scikit-learn, 'A tutorial on statistical-learning for scientific data processing'. [Online].
Available: https://scikit-learn.org/stable/tutorial/statistical_inference/