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Model for Classification of Poems in Hindi Language Based on Ras
The developed model will classify poem into Shringar, Hasya, Adbhuta, Shanta, Raudra, Veera, Karuna, Bhayanaka, Vibhasta rasas, which will use mix of part-of-speech-based feature and emotional
Multi - Class Document Classification: Effective and Systematized Method to Categorize Documents
TLDR
This research work is combining approach of Natural Language Processing and Machine Learning for content-based classification of documents that is successful in classifying documents with more than 70% of accuracy for major Indian Languages and more than 80% accuracy for English Language.
Emotion Classification with Reduced Feature Set SGDClassifier, Random Forest and Performance Tuning
TLDR
This research work is classifying emotions written in Hindi in form of poem with 4 categories namely Karuna, Shanta, Shringar and Veera, the model is build with Random Forest, SGDClassifier and was trained with 134 poetries and tested with 46 Poetries for both types of features.
Data Classification with k-fold Cross Validation and Holdout Accuracy Estimation Methods with 5 Different Machine Learning Techniques
TLDR
The result of the experiment shows that the results of SVM, NB and random forest methods are better as compared to DTT and K-NN for used data set available in this experiment.
A Study of Current State of Work and Challenges in Mining Big Data
TLDR
The current status of Mining Big data and challenges in mining big data in coming years are discussed and the capability of extracting useful information from large data sets was not earlier possible due to its complexity.
Automatic Multiclass Document Classification of Hindi Poems using Machine Learning Techniques
  • Kaushika Pal, B. Patel
  • Computer Science
    International Conference for Emerging Technology…
  • 1 June 2020
TLDR
Experiments shows that Naïve Bayes with 64% accuracy and Random Forest with 56% are performing better as compared to other algorithms for Hindi Poem Classification.
A Study of Current State of Work done for Classification in Indian Languages
TLDR
The purpose of this paper is to study current work done in various Indian languages, and analyze the current situation and future scope to research in classification and related work on Indian languages.
A Novel Evolving Sentimental Bag-of-Words Approach for Feature Extraction to Detect Misinformation
—The state-of-the-art misinformation detection techniques mainly focus on static datasets. However, a massive amount of information is generated online and the websites are flooded with this
A Novel Framework for Sanskrit-Gujarati Symbolic Machine Translation System
—Sanskrit falls under the Indo-European language family category. Gujarati, which has descended from the Sanskrit language, is a widely spoken language particularly in the Indian state of Gujarat.
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