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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Roopa, Chaluvegowda Kanakalakshmi
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Computer Science & IT Engineering

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Evaluating the influence of feature selection-based dimensionality reduction on sentiment analysis Kishore, Gowrav Ramesh Babu; Harish, Bukahally Somashekar; Roopa, Chaluvegowda Kanakalakshmi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3366-3374

Abstract

As social media has become an integral part of digital medium, the usage of the same has increased multi-fold in recent years. With increase in usage, the sentiment analysis of such data has emerged as one of the most sought research domains. At the same time, social media texts are known to pose variety of challenges during the analysis, thus making pre-processing one of the important steps. The aim of this work is to perform sentiment analysis on social media text, while handling the noise effectively in the data. This study is performed on a multi-class twitter sentiment dataset. Firstly, we apply several text cleaning techniques in order to eliminate noise and redundancy in the data. In addition, we examine the influence of regularized locality preserving indexing (RLPI) technique combined with the well-known word weighting methods. The findings obtained from experiment indicate that, RLPI outperforms other algorithms in feature selection and when paired with long short-term memory (LSTM), the combination outperforms other classification models that are discussed.
Classification of Kannada documents using novel semantic symbolic representation and selection method Rangan, Ranganathbabu Kasturi; Harish, Bukahally Somashekar; Roopa, Chaluvegowda Kanakalakshmi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3354-3365

Abstract

Kannada is one of the 22 scheduled Indian regional languages. It is also a low-resource regional language. The Kannada document classification is arduous due to its vocabulary richness, agglutinative terms, and lack of resources. The good representation and the prominent feature selection aid in solving the challenges in document classification tasks. In this paper, we are proposing semantic symbolic representation and feature selection method, for better representation of Kannada terms in interval values embedded with positional information. Following, selection of prominent discriminative symbolic feature vectors is also proposed. Further the symbolic document classifier is used to classify the Kannada documents. The proposed cluster based symbolic representation preserves the intra class variance and reduces the ambiguity in classification of Kannada documents. The experiments are performed over two Kannada document datasets which are multilabel and unbalanced. The comparative analysis of proposed method with other standard methods is also presented.
Co-Authors Harish, Bukahally Somashekar Kishore, Gowrav Ramesh Babu Rangan, Ranganathbabu Kasturi