Twitter sentiment analysis model using multiple classifiers on a kaggle dataset.
In this project, we aim to develop a Twitter sentiment analysis model using Python to identify the sentiments expressed by people towards Pfizer vaccines. Twitter sentiment analysis allows us to analyze public opinions and emotions related to various topics, and in this case, we will focus on analyzing sentiments specifically towards Pfizer vaccines.
we will utilize a dataset available on Kaggle, which contains a collection of tweets related to Pfizer vaccines. This dataset will serve as the foundation for creating our machine learning model. We will extract relevant features from the collected tweets and employ machine learning techniques to analyze and classify the sentiments expressed within them.
- link to the dataset:"https://www.kaggle.com/datasets/gpreda/pfizer-vaccine-tweets?resource=download"
The sentiments identified in this project will include positive, negative, and neutral sentiments. By analyzing the text of each tweet, we will train our model to accurately categorize them into these sentiment categories. This classification process will enable us to gain insights into the prevailing sentiments towards Pfizer vaccines on Twitter.
To evaluate the performance of our sentiment analysis model, we will utilize various classifiers. By experimenting with different classifiers such as Naive Bayes, Support Vector Machines (SVM), and Random Forest, among others, we aim to identify the classifier that provides the highest model accuracy. This comparison will enable us to determine the most effective approach for sentiment analysis in the context of Pfizer vaccines.
Throughout the project, we will follow industry-standard practices for data preprocessing, model training, and evaluation. We will split the dataset into training and testing sets to validate the model's performance.
The successful completion of this project will yield a reliable sentiment analysis model capable of accurately categorizing tweets into positive, negative, or neutral sentiments towards Pfizer vaccines. This model will provide valuable insights into public sentiments and opinions, facilitating a better understanding of how people perceive Pfizer vaccines on Twitter