How can you increase the accuracy of a text classification?

How can you increase the accuracy of a text classification?

In this article, I’ve illustrated the six best practices to enhance the performance and accuracy of a text classification model which I had used:

1. Domain Specific Features in the Corpus.
2. Use An Exhaustive Stopword List.
3. Noise Free Corpus.
4. Eliminating features with extremely low frequency.
5. Normalized Corpus.

How can you improve the accuracy of NLP?

8 Methods to Boost the Accuracy of a Model

1. Add more data. Having more data is always a good idea.
2. Treat missing and Outlier values.
3. Feature Engineering.
4. Feature Selection.
5. Multiple algorithms.
6. Algorithm Tuning.
7. Ensemble methods.

How do you classify in Weka?

Start the Weka Explorer:

1. Open the Weka GUI Chooser.
2. Click the “Explorer” button to open the Weka Explorer.
3. Load the Ionosphere dataset from the data/ionosphere. arff file.
4. Click “Classify” to open the Classify tab.

How do you improve text analysis?

Three Strategies for Improving Analysis of Texts

1. Compare and contrast events, characters or settings. This requires moving past focusing on the elements that are clearly visible and obvious and considering implied similarities and differences that are not explicitly stated in the text.
2. Identify the theme.

How do you evaluate the effectiveness of text classification models?

Cross-validation is a common method to evaluate the performance of a text classifier. It works by splitting the training dataset into random, equal-length example sets (e.g., 4 sets with 25\% of the data). For each set, a text classifier is trained with the remaining samples (e.g., 75\% of the samples).

Where do we use classification?

Classification Algorithms can be used to solve classification problems such as Identification of spam emails, Speech Recognition, Identification of cancer cells, etc. The regression Algorithm can be further divided into Linear and Non-linear Regression.

How do you implement classification in machine learning?

Algorithm Selection

1. Read the data.
2. Create dependent and independent data sets based on our dependent and independent features.
3. Split the data into training and testing sets.
4. Train the model using different algorithms such as KNN, Decision tree, SVM, etc.
5. Evaluate the classifier.
6. Choose the classifier with the most accuracy.

How to use weka to test your ideas?

Anyway, that’s what WEKA is all about. It allows you to test your ideas quickly. To see the visual representation of the results, right click on the result in the Result list box. Several options would pop up on the screen as shown here − Select Visualize tree to get a visual representation of the traversal tree as seen in the screenshot below −

How can I improve the accuracy of my text classification model?

Generally, in text classification, in my opinion, the most influential factor to improve the accuracy is the quality of the features. As for as you are using SVM try Linear kernel which performs better results in text classification if you have not already tested your model with different kernels.

What are the steps involved in text classification?

Generally, there are a lot of steps and possibilities in text classification, including data collection, preparation, transformation, processing with classification algorithms, changing the parameters of these algorithms etc. It is therefore not easy to give you a specific and reliable answer.

How to plot results of classification using visualize classifier errors?

Selecting Visualize classifier errors would plot the results of classification as shown here − A cross represents a correctly classified instance while squares represents incorrectly classified instances. At the lower left corner of the plot you see a cross that indicates if outlook is sunny then play the game.