Suitability of Naïve Bayesian Methods for Paragraph Level Text Classification in the Kannada Language using Dimensionality Reduction Technique

Jayashree, R.; K, Srikantamurthy; Anami, Basavaraj S.

doi:10.5121/ijaia.2013.4509

Cited by 4 publications

(1 citation statement)

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“…India is a highly multilingual country with 22 constitutionally recognized languages. Text miningresearcheshavebeenconductedforallthepopularlanguages,i.e., Hindi(Bakliwaletal., 2012;Dutta, Kaushik, & Prakash, 2011;Joshi et al, 2010;Raghuveer & Murthy, 2007), Tamil (Raghuveer&Murthy,2007;Swamy&Hanumanthappa,2013),Telugu(Durga&Govardhan,2011Murthy,2005;Raghuveer&Murthy,2007;Swamy&Hanumanthappa,2013),Kannada (Jayashree, Srikantamurthy,&B.,2013;Raghuveer&Murthy,2007;Swamy&Hanumanthappa,2013),Panjabi (Nidhi&Gupta,2012;Raghuveer&Murthy,2007),andBengali (Mansur,UzZama,&Khan,2006;Raghuveer&Murthy,2007).However,mostoftheresearchersoftextminingontheIndianlanguage haveconductedresearchonlyonthetextcategorization (Jayashree,R.,&Srikanta,2011;Jayashreeet al,2013;Murthy,2005;Raghuveer&Murthy,2007;Swamy&Hanumanthappa,2013).Hence,we findveryfewstudiesonsentimentunearthing (Bakliwaletal.,2012;Joshietal.,2010).Considering theimportanceofdigitalexpression,itsfuturepotential,andapplicabilityoftextminingtechniques, wearguethisastheveryvitalgapintheliterature.…”

Section: Literature Reviewmentioning

confidence: 99%

Political Sentiment Mining

Bele

Panigrahi

Srivastava

2017

International Journal of Business Intelligence Research

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Investigations on sentiment mining are mostly ensued in the English language. Due to the characteristics of the Indian languages tools and techniques used for sentiment mining in the English language cannot be applied directly to text in Hindi languages. The objective of this paper is to extract the political sentiment at the document-level from Hindi blogs. The authors could not find any literature about extracting sentiments at the document-level from Hindi blogs. They extracted opinion about one of India's very famous leaders who was a prominent face in the national election of 2014. They prepared the datasets from Hindi blogs reviews. They purposed the lexicon and machine learning technique to classify the sentiment. Their purposed method used four steps: (1) Crawling and preprocessing the blog reviews; (2) Extracting reviews relevant to the query using the Vector Space Model (VSM); (3) Identifying sentiment at the document level using the Lexicon method, and (4) Measuring the result using the Machine learning technique. Their experimental result demonstrates the effectiveness of our algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Political Sentiment Mining

Bele

Panigrahi

Srivastava

2017

International Journal of Business Intelligence Research

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Text Analysis and Classification for Preprocessing Phase of Automatic Text Summarization Systems

KADAM

Khandale

2022

Soft Computing and Its Engineering Applications

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Classification Accuracy by Deviation-based Classification Method with the Number of Training Documents

Lee

2014

Journal of Digital Convergence

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This paper presents a novel approach at the intersection of machine learning and number theory, focusing on the classification of prime and non-prime numbers. At the core of our research is the development of a highly sparse encoding method, integrated with conventional neural network architectures. This combination has shown promising results, achieving a recall of over 99% in identifying prime numbers and 79% for nonprime numbers from an inherently imbalanced sequential series of integers, while exhibiting rapid model convergence before the completion of a single training epoch. An interesting aspect of our findings includes the analysis of false positives, where we found a consistent misclassification of certain non-prime numbers, particularly semi-primes. We performed training using 10 6 integers starting from a specified integer and tested on a different range of 2 × 10 6 integers extending from 10 6 to 3 × 10 6 , offset by the same starting integer. While constrained by the memory capacity of our resources, which limited our analysis to a span of 3 × 10 6 , we believe that our study contribute to the application of machine learning in prime number analysis. This work aims to demonstrate the potential of such applications and hopes to inspire further exploration and possibilities in diverse fields.

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Suitability of Naïve Bayesian Methods for Paragraph Level Text Classification in the Kannada Language using Dimensionality Reduction Technique

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Cited by 4 publications

References 2 publications

Political Sentiment Mining

Political Sentiment Mining

Text Analysis and Classification for Preprocessing Phase of Automatic Text Summarization Systems

Classification Accuracy by Deviation-based Classification Method with the Number of Training Documents

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