Access the full text.
Sign up today, get DeepDyve free for 14 days.
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
Document classification has played a major role in many fields like information retrieval, data mining, etc. where machine learning and deep learning models can be applied. But, before applying any model for classification, textual data must be converted into a numerical measure, where word embedding can help. The selection of appropriate word embedding techniques plays a vital role in classification. So, we analysed the classification performance by widely used deep learning models long short-term memory (LSTM) and convolution neural network (CNN) with various word embedding techniques on five benchmark datasets. The pre-processed dataset is converted into vector representation using a word embedding techniques TF-IDF, Word2Vec, and Doc2Vec. The output is given to the LSTM and CNN classifier and documents are classified as per their context. The CNN classifier with Doc2Vec word embedding technique achieves almost 12% more accuracy as compared to other word embedding techniques on all the datasets.
International Journal of Web Engineering and Technology – Inderscience Publishers
Published: Jan 1, 2022
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.