Build, train, test an Artificial Intelligence (AI) model to predict sentiment from thousands of tweets. Sentiment prediction involves understanding people's feelings about a product or service.
AI-ML sentiment analysis tools empower companies to automatically predict whether their customers are happy. The process could be done automatically without humans manually reviewing thousands of tweets/reviews.
- Twitter tweets (text data)
- Sentiment (0 or 1)
- Natural Language Processing (NLP) works by converting words(text) into numbers
- These numbers are then used to train an AI/ML model to make predictions.
- Predictions could be sentiments inferred from social media posts and product reviews.
- AI/ML-based sentiment analysis is crucial for companies to predict whether their customers are happy or not automatically.
- The process could be done automatically without humans manually reviewing thousands of tweets and customer reviews.
- In this case study, we will analyze thousands of Twitter tweets to predict people's sentiments.
1: Understand the Problem Statement and business case
2: Import libraries and datasets
3: Perform Exploratory Data Analysis
4: Perform Data Cleaning
5: Visualize Cleaned Datasets
6: Prepare the data by applying a count vectorizer
7: Train a Naive Bayes Classifier
8: Assess trained model performance
In this NLP-based sentiment analysis project, I learned to preprocess Twitter data, extract features using TF-IDF, and implement machine learning models for sentiment classification. Key skills included handling imbalanced data, evaluating models with accuracy and F1-score, and applying sentiment analysis to understand public opinion and customer feedback. This hands-on experience solidified my ability to use NLP for real-world sentiment analysis