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Punjabi Poetry Classification: The Test of 10 Machine Learning Algorithms
TLDR
Results for Punjabi poetry classification revealed that 4 machine learning algorithms namely, Hyperpipes (HP), K- nearest neighbour (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) with an accuracy of 50.63 %, 52.75 % and 58.79 % respectively, outperformed all other machinelearning algorithms under the test.
A Study of Text Classification Natural Language Processing Algorithms for Indian Languages
TLDR
This study shows that supervised learning algorithms (Naive Bayes (NB), Support Vector Machine (SVM), Artificial Neural Network (ANN), and N-gram) performed better for Text Classification task.
Emotion Detection and Sentiment Analysis in Text Corpus: A Differential Study with Informal and Formal Writing Styles
TLDR
A differential analysis of Formal and Informal text pieces in the field of Sentiment Classification and differences of approaches used for Emotion Detection and Sentiment Analysis for both cases are presented.
A Study and Analysis of Opinion Mining Research in Indo-Aryan, Dravidian and Tibeto-Burman Language Families
TLDR
A study and analysis of different languages used for emotion detection and sentiment analysis for formal and informal piece of writing in India and performance comparison of Indian languages with world language, that is, English.
Machine Transliteration system in Indian perspectives
This paper addresses the various progresses in Indian Machine Transliteration systems, which is considered as a very important part for many natural language processing applications. Transliteration
Automatic classification of Punjabi poetries using poetic features
TLDR
A content-based Punjabi poetry classifier was built utilising Weka toolset using poetic features, and the best performing algorithm is SVM and highest accuracy is achieved considering orthographic features.
Punjabi Stop Words: A Gurmukhi, Shahmukhi and Roman Scripted Chronicle
TLDR
For the first time in scientific community dealing with computational linguistics and literature processing using NLP techniques, the list of 184 stop words of Punjabi language is released for public usage and further NLP applications.
A Natural Language Processing Approach for Identification of Stop Words in Punjabi Language
TLDR
This paper concentrates on identification of stop words from poetry and other news articles and discusses the importance of each sub-phase in Punjabi poetry.
An Analysis of Opinion Mining Research Works Based on Language, Writing Style and Feature Selection Parameters
TLDR
A study and analysis of differences of approaches used for Opinion Mining and Sentiment Analysis for both cases and it was found that parameter, IG and TF-IDF, were experimented maximum number of times and IG outperformed all other feature selectors.
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