Multi-label Text Categorization using Error-correcting Output Coding with Weighted Probability

Balamurugan, V.; Vedanarayanan, V.; Nisha, A. Sahaya Anselin; Narmadha, R.; Amirthalakshmi, T. M.

doi:10.5829/ije.2022.35.08b.08

Cited by 3 publications

(4 citation statements)

References 25 publications

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“…We are also working on two classes: hostile and nonhostile. Since we are getting comparatively good results for the Logistic regression method (36)(37)(38)(39). We have also compared the result of Bag-of-Words and TF-IDF feature extraction methods.…”

Section: Resultsmentioning

confidence: 99%

“…Bag-of-Words consist of: (i) A measure of the presence of words in the document (ii) A Vocabulary of words from the document. • TF-IDF (27,28,37): One of the most effective feature extraction methods is TF-IDF. TF-IDF stands for Term Frequency -Inverse Document Frequency.…”

Section: Feature Extraction Using Bag-of-words and Tf-idf Modelmentioning

confidence: 99%

“…Figure 1 shows basic approaches used for hostile post-detection. Mainly these approaches are divided into two major categories: (i) Machine Learning based (23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) and (ii) Deep Learning based (5,6,(27)(28)(29)(34)(35)(36)(37)(38). Machine learning-based approach is categorized into two subparts:(i) Supervised machine learning based and (ii) Unsupervised machine learning based (27,(30)(31)(32)37).…”

Section: Introductionmentioning

confidence: 99%

“…Mainly these approaches are divided into two major categories: (i) Machine Learning based (23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) and (ii) Deep Learning based (5,6,(27)(28)(29)(34)(35)(36)(37)(38). Machine learning-based approach is categorized into two subparts:(i) Supervised machine learning based and (ii) Unsupervised machine learning based (27,(30)(31)(32)37). Support vector machine (SVM) (39), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbour (KNN), Gaussian Naive Bayes (GNB) and Logistic Regression (LR) algorithms are used for supervised learning (24).…”

Section: Introductionmentioning

confidence: 99%

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Investigating Hostile Post Detection in Gujarati: A Machine Learning Approach

Rameshbhai,

Rana

2024

IJE

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Hostile post on social media is a crucial issue for individuals, governments and organizations. There is a critical need for an automated system that can investigate and identify hostile posts from large-scale data. In India, Gujarati is the sixth most spoken language. In this work, we have constructed a major hostile post dataset in the Gujarati language. The data are collected from Twitter, Instagram and Facebook. Our dataset consists of 1,51,000 distinct comments having 10,000 manually annotated posts. These posts are labeled into the Hostile and Non-Hostile categories. We have used the dataset in two ways: (i) Original Gujarati Text Data and (ii) English data translated from Gujarati text. We have also checked the performance of pre-processing and without pre-processing data by removing extra symbols and substituting emoji descriptions in the text. We have conducted experiments using machine learning models based on supervised learning such as Support Vector Machine, Decision Tree, Random Forest, Gaussian Naive-Bayes, Logistic Regression, K-Nearest Neighbor and unsupervised learning based model such as k-means clustering. We have evaluated performance of these models for Bag-of-Words and TF-IDF feature extraction methods. It is observed that classification using TF-IDF features is efficient. Among these methods Logistic regression outperforms with an Accuracy of 0.68 and F1-score of 0.67. The purpose of this research is to create a benchmark dataset and provide baseline results for detecting hostile posts in Gujarati Language.

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Section: Resultsmentioning

confidence: 99%

Section: Feature Extraction Using Bag-of-words and Tf-idf Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating Hostile Post Detection in Gujarati: A Machine Learning Approach

Rameshbhai,

Rana

2024

IJE

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Semantic features analysis for biomedical lexical answer type prediction using ensemble learning approach

Hussain,

Wasim,

Cheema

et al. 2024

Knowl Inf Syst

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Lexical answer type prediction is integral to biomedical question–answering systems. LAT prediction aims to predict the expected answer’s semantic type of a factoid or list-type biomedical question. It also aids in the answer processing stage of a QA system to assign a high score to the most relevant answers. Although considerable research efforts exist for LAT prediction in diverse domains, it remains a challenging biomedical problem. LAT prediction for the biomedical field is a multi-label classification problem, as one biomedical question might have more than one expected answer type. Achieving high performance on this task is challenging as biomedical questions have limited lexical features. One biomedical question must be assigned multiple labels given these limited lexical features. In this paper, we develop a novel feature set (lexical, noun concepts, verb concepts, protein–protein interactions, and biomedical entities) from these lexical features. Using ensemble learning with bagging, we use the label power set transformation technique to classify multi-label. We evaluate the integrity of our proposed methodology on the publicly available multi-label biomedical questions dataset (MLBioMedLAT) and compare it with twelve state-of-the-art multi-label classification algorithms. Our proposed method attains a micro-F1 score of 77%, outperforming the baseline model by 25.5%.

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Text classification and its application based on natural language processing and machine learning

Jin,

Zhang

2024

AIP Conference Proceedings

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Multi-label Text Categorization using Error-correcting Output Coding with Weighted Probability

Cited by 3 publications

References 25 publications

Investigating Hostile Post Detection in Gujarati: A Machine Learning Approach

Investigating Hostile Post Detection in Gujarati: A Machine Learning Approach

Semantic features analysis for biomedical lexical answer type prediction using ensemble learning approach

Text classification and its application based on natural language processing and machine learning

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