Twitter Sentimental Analysis (NLP) | Machine Learning - Videos | GeeksforGeeks

Data Analysis is the process to analyze the data and find out the answers to how and why things happened in the past. With this past data, we can create a machine learning module to predict future outcomes. In this video, we are going to analyze Twitter sentiment using Python in Machine Learning. In this project, we are going to train machine learning models with loads of twitter data and the model can predict whether the tweets are positive or negative.

Work Flow -

1) Data Collection: Data Collection is the process of collecting, extracting, and storing the data which may be structured or unstructured forms like text, video, audio, XML files, records, or other image files used in later stages of data analysis.

Related Article: https://www.geeksforgeeks.org/different-sources-of-data-for-data-analysis/ https://www.geeksforgeeks.org/collection-and-presentation-of-data/

2) Data Processing: It is a general process of data to convert into another format which must be informative data. Process of conversion of data in the computer understandable format. To perform analysis on data we need proper data. Here we will use the diabetes dataset and import this dataset into our code using NumPy.

Related Article: https://www.geeksforgeeks.org/introduction-to-data-processing/

3) Train Test Split: It is used to estimate the performance of machine learning algorithms that are applicable for prediction-based Algorithms/Applications.

Related Article: https://www.geeksforgeeks.org/how-to-split-a-dataset-into-train-and-test-sets-using-python/

4) Logistic Regression model: It is a supervised classification algorithm that is used when the target variable is categorical.

Related Article: https://www.geeksforgeeks.org/understanding-logistic-regression/

5) Trained Logistic Regression model: After preparing the model we will train the model to prepare the tweet sentiment with Positive tweet or negative tweet prediction.