machine learning classifier for sentiment analysis
Budget: $10 – $30 USD
Develop a machine learning classifier for sentiment analysis. Based on the dataset , write a python program to implement a sentiment classifier and evaluate its performance. Notice:80% data for training and 20% data for testing.
(1) Features used for sentiment classification and explain why you select these features.
(2) Select two of the supervised learning algorithm from scikit-learn library: https://scikit-learn.org/stable/supervised_learning.html#supervised-learning, to build a sentiment classifier respectively.
(3) Compare the performance over accuracy, precision, recall, and F1 score for the two algorithms you selected. Here is the reference of how to calculate these metrics: https://towardsdatascience.com/accuracy-precision-recall-or-f1-331fb37c5cb9.
I need all the above details .
(1) Features used for sentiment classification and explain why you select these features.
(2) Select two of the supervised learning algorithm from scikit-learn library: https://scikit-learn.org/stable/supervised_learning.html#supervised-learning, to build a sentiment classifier respectively.
(3) Compare the performance over accuracy, precision, recall, and F1 score for the two algorithms you selected. Here is the reference of how to calculate these metrics: https://towardsdatascience.com/accuracy-precision-recall-or-f1-331fb37c5cb9.
I need all the above details .