How do you do sentiment analysis in Weka?

Hands-on WEKA

1. Step 1: Get the dataset. I got the data from here. The dataset has four fields, “tweet_id”, “sentiment”, “author” and “content”.
2. Step 2: Data Preprocessing.
3. Step 3: Classification.
4. Step 4: Results. The processing is done, for the visualization part, we have Visualization. TextViewer.

How do you do sentiment analysis step by step?

Sentiment Analysis Process

1. Step 1: Data collection.
2. Step 2: Data processing.
3. Step 3: Data analysis.
4. Step 4 – Data visualization.
5. Step 1 – Register & Create Project.
6. Step 2 – Link/Upload & Process Data.
7. Step 3 – Visualise Data.
8. Step 4 – Training your Model without Coding.

How do you do sentiment analysis for data?

Steps to build Sentiment Analysis Text Classifier in Python

1. Data Preprocessing. As we are dealing with the text data, we need to preprocess it using word embeddings.
2. Build the Text Classifier. For sentiment analysis project, we use LSTM layers in the machine learning model.
3. Train the sentiment analysis model.

How do you text a classification?

Text Classification Workflow

1. Step 1: Gather Data.
2. Step 2: Explore Your Data.
3. Step 2.5: Choose a Model*
4. Step 3: Prepare Your Data.
5. Step 4: Build, Train, and Evaluate Your Model.
6. Step 5: Tune Hyperparameters.
7. Step 6: Deploy Your Model.

What is the best model for text classification?

Linear Support Vector Machine is widely regarded as one of the best text classification algorithms. We achieve a higher accuracy score of 79\% which is 5\% improvement over Naive Bayes.

What is SMO in Weka?

SMO refers to the specific efficient optimization algorithm used inside the SVM implementation, which stands for Sequential Minimal Optimization. Weka Configuration for the Support Vector Machines Algorithm.

How do you use Weka for text classification?

Starts here59:20Weka Text Classification for First Time & Beginner Users – YouTubeYouTube