LSI with Support Vector Machine for Text Categorization – a practical example with Python

Artificial intelligence is becoming a powerful tool of modernity science, there is even a science consensus about how our society is turning to a data-driven society. Machine learning is a branch of Artificial intelligence that has the ability to learn from data and understand its behavers. Python programming language aiming the challenges of this new era is becoming one of the most popular languages for general programming and scientific computing. Keeping all this new era circumstances in mind, this article has as a goal to show one example of how to use one supervised machine learning method, Support Vector Machine, and to predict movie’s genre according to its description using the programming language of the moment, python. Firstly, Omdb official API was used to gather data about movies, then tuned Support Vector Machine model for Latent semantic indexing capable of predicting movies genres according to its plot was coded. The performance of the model occurred to be satisfactory considering the small dataset used and the occurrence of movies with hybrid genres. Testing the model with larger dataset and using multi-label classification models were purposed to improve the model.

Book Genre Prediction

The present work aims to classify the genre of the books automatically using the Python programming language. A genre is a subset of art, literature, or music that has a distinct form, substance, and style. In many instances, a book can be classified as belonging to more than one genre. It's difficult to categorize a book or piece of literature as belonging to one genre over another. Many novels end up badly categorized or pushed under the super-genre umbrella of fiction since there is no clear criterion to determine how much of a book belongs to a given genre. Therefore, it's critical to develop a system for categorizing books and determining their relevance to a particular genre. Therefore, the current study tries to solve this challenge by combining various text categorization approaches and models to come up with the best solution

PERFECTIONOF CLASSIFICATION ACCURACY IN TEXT CATEGORIZATION

Problems and strategies for text classification have already been known for a long time. Theyre widely utilised by companies like Google and Yahoo for email spam screening, sentiment analysis of Twitter data, and automatic news categories in Google alerts. Were still working on getting the findings to be as accurate as possible. When dealing with large amounts of text data, however, the models performance and accuracy become a difficulty. The type of words utilised in the corpus and the type of features produced for classification have a big impact on the performance of a text classification model.