A survey on classification techniques for text mining

Brindha, S.; Prabha, K.; Sukumaran, S.

doi:10.1109/icaccs.2016.7586371

Cited by 41 publications

(19 citation statements)

References 7 publications

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“…This is mainly due to the problem of ambiguity in natural languages. The issues like Polysemy (one word-multiple meanings) and synonymy (multiple words-similar meaning) are two prominent issues in text mining (Brindha et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…It is also difficult to process every bit of data manually and classify them clearly. This led to the emergence of intelligent tools in text processing, in the field of natural language processing, to analyze lexical and linguistic patterns (Brindha et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Text mining/ analytics analyzes the hitherto hidden relationships between entities in a dataset to derive meaningful patterns which reflect the knowledge contained in the dataset. This knowledge is utilized in decision making (Brindha, Sukumaran, & Prabha, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Text Classification Techniques: A Literature Review

Thangaraj

Sivakami

2018

IJIKM

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Aim/Purpose: The aim of this paper is to analyze various text classification techniques employed in practice, their strengths and weaknesses, to provide an improved awareness regarding various knowledge extraction possibilities in the field of data mining. Background: Artificial Intelligence is reshaping text classification techniques to better acquire knowledge. However, in spite of the growth and spread of AI in all fields of research, its role with respect to text mining is not well understood yet. Methodology: For this study, various articles written between 2010 and 2017 on “text classification techniques in AI”, selected from leading journals of computer science, were analyzed. Each article was completely read. The research problems related to text classification techniques in the field of AI were identified and techniques were grouped according to the algorithms involved. These algorithms were divided based on the learning procedure used. Finally, the findings were plotted as a tree structure for visualizing the relationship between learning procedures and algorithms. Contribution: This paper identifies the strengths, limitations, and current research trends in text classification in an advanced field like AI. This knowledge is crucial for data scientists. They could utilize the findings of this study to devise customized data models. It also helps the industry to understand the operational efficiency of text mining techniques. It further contributes to reducing the cost of the projects and supports effective decision making. Findings: It has been found more important to study and understand the nature of data before proceeding into mining. The automation of text classification process is required, with the increasing amount of data and need for accuracy. Another interesting research opportunity lies in building intricate text data models with deep learning systems. It has the ability to execute complex Natural Language Processing (NLP) tasks with semantic requirements. Recommendations for Practitioners: Frame analysis, deception detection, narrative science where data expresses a story, healthcare applications to diagnose illnesses and conversation analysis are some of the recommendations suggested for practitioners. Recommendation for Researchers: Developing simpler algorithms in terms of coding and implementation, better approaches for knowledge distillation, multilingual text refining, domain knowledge integration, subjectivity detection, and contrastive viewpoint summarization are some of the areas that could be explored by researchers. Impact on Society: Text classification forms the base of data analytics and acts as the engine behind knowledge discovery. It supports state-of-the-art decision making, for example, predicting an event before it actually occurs, classifying a transaction as ‘Fraudulent’ etc. The results of this study could be used for developing applications dedicated to assisting decision making processes. These informed decisions will help to optimize resources and maximize benefits to the mankind. Future Research: In the future, better methods for parameter optimization will be identified by selecting better parameters that reflects effective knowledge discovery. The role of streaming data processing is still rarely explored when it comes to text classification.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Text Classification Techniques: A Literature Review

Thangaraj

Sivakami

2018

IJIKM

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“…This algorithm concentrate on specific features of the document to be classified [8]. It is effective and easy to implement [18] and also the most accepted algorithms for pattern recognition [16].…”

Section: K-nearest Neighbormentioning

confidence: 99%

“…Naive Bayes algorithm is one of classification technique that makes exploit of statistical approach and based on the conditional probabilities for the problems of pattern recognition [16]. Naive Bayes uses Bayes Theorem concept [15][16] [18] with strong independence assumptions.…”

Section: Naive Bayesmentioning

confidence: 99%

Comparative Study of Machine Learning Approach on Malay Translated Hadith Text Classification based on Sanad

et al. 2017

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Abstract. Sanad is one of important part used to determine the authentication of hadith. However, very little research work has been found on classification of Malay translated Hadith based on sanad. There are some researches done using machine learning approach on hadith classification based on sanad but using different objective with different language. This research is to see how Machine Learning techniques are used to classify Malay translated Hadith document based on sanad. In this paper, SVM, NB and k-NN are used to identify and evaluate the performance of Malay translated hadith based on sanad. The performances are evaluated based on standard performance metrics used in text classification which is accuracy and response time. The results show that SVM has the highest accuracy and k-NN has the best response time (time taken in process for classification data) compare to other classifier. In future, we plan to extend this paper with the analysis on interclass similarity and also test on larger dataset.

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Lexicon‐pointed hybrid N‐gram Features Extraction Model (LeNFEM) for sentence level sentiment analysis

Mutinda

Mwangi

Okeyo

2021

Engineering Reports

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Sentiment analysis of social media textual posts can provide information and knowledge that is applicable in social settings, business intelligence, evaluation of citizens' opinions in governance, and in mood triggered devices in the Internet of Things. Feature extraction and selection is a key determinant of accuracy and computational cost of machine learning models for such analysis. Most feature extraction and selection techniques utilize bag of words, N‐grams, and frequency‐based algorithms especially Term Frequency‐Inverse Document Frequency. However, these approaches do not consider relationships between words, they ignore words' characteristics and they suffer high feature dimensionality. In this paper we propose and evaluate a feature extraction and selection approach that utilizes a fixed hybrid N‐gram window for feature extraction and minimum redundancy maximum relevance feature selection algorithm for sentence level sentiment analysis. The approach improves the existing features extraction techniques, specifically the N‐gram by generating a hybrid vector from words, Part of Speech (POS) tags, and word semantic orientation. The vector is extracted by using a static trigram window identified by a lexicon where a sentiment word appears in a sentence. A blend of the words, POS tags, and the sentiment orientations of the static trigram are used to build the feature vector. The optimal features from the vector are then selected using minimum redundancy maximum relevance (MRMR) algorithm. Experiments were carried out using the public Yelp dataset to compare the performance of the proposed model and existing feature extraction models (BOW, normal N‐grams and lexicon‐based bag of words semantic orientations). Using supervised machine learning classifiers the experimental results showed that the proposed model had the highest F‐measure (88.64%) compared to the highest (83.55%) from baseline approaches. Wilcoxon test carried out ascertained that the proposed approach performed significantly better than the baseline approaches. Comparative performance analysis with other datasets further affirmed that the proposed approach is generalizable.

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A survey on classification techniques for text mining

Cited by 41 publications

References 7 publications

Text Classification Techniques: A Literature Review

Text Classification Techniques: A Literature Review

Comparative Study of Machine Learning Approach on Malay Translated Hadith Text Classification based on Sanad

Lexicon‐pointed hybrid N‐gram Features Extraction Model (LeNFEM) for sentence level sentiment analysis

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