Text Document Preprocessing with the Bayes Formula for Classification Using the Support Vector Machine

Isa, Dino; Lee, Lam Hong; Kallimani, Vish; Rajkumar, R.

doi:10.1109/tkde.2008.76

Cited by 193 publications

(134 citation statements)

References 8 publications

Supporting

Mentioning

122

Contrasting

Order By: Relevance

“…In this stage the dataset get normalized and prepared for the classification algorithm so that the particular algorithm can run smoothly and bring effective results in minimum time [8]. According to many researches, parameters for pre-processing includes TF-IDF, Stemmer, stopwords Handler and tokenizer etc [1], [25], [30]. In this study we have used the default parameters for preprocessing as shown in Figure 2.…”

Section: Pre-processingmentioning

confidence: 99%

Sentiment Analysis of Tweets using SVM

Ahmad¹,

Aftab²,

Ali³

2017

IJCA

110

View full text Add to dashboard Cite

Community's view and feedback have always proved to be the most essential and valuable resource for companies and organizations. With social media being the emerging trend among everyone, it paves way for unprecedented analysis and evaluation of various aspects for which organizations had to rely on unconventional, time consuming and error prone methods earlier. This technique of analysis directly falls under the domain of "sentiment analysis". Sentiment analysis encompasses the vast field of effective classification of user generated text under defined polarities. There are several tools and algorithms available to perform sentiment detection and analysis including supervised machine learning algorithms that perform classification on the target corpus, after getting trained with training data. Lexical techniques which performs classification on the basis of dictionary based annotated corpus and Hybrid tools which are combination of machine learning and lexicon based algorithms. In this paper we have used Support Vector Machine (SVM) for sentiment analysis in Weka. SVM is one of the widely used supervised machine learning algorithms for textual polarity detection. To analyze the performance of SVM, two pre classified datasets of tweets are used and for comparative analysis, three measures are used: Precision, Recall and F-Measure. Results are shown in the form of tables and graphs.

show abstract

Section: Pre-processingmentioning

confidence: 99%

Sentiment Analysis of Tweets using SVM

Ahmad¹,

Aftab²,

Ali³

2017

IJCA

110

View full text Add to dashboard Cite

show abstract

“…Isa et al, in [24] used the Bayes formula to represent the document as a set of vectors according to a probability division revealing the categories that the document possibly will belong to. This probability distribution as the vectors to represent the document, the SVM is used to classify the documents.…”

Section: Backgroungmentioning

confidence: 99%

Arabic Documents classification method a Step towards Efficient Documents Summarization

Ahmed¹

2015

IJRITCC

View full text Add to dashboard Cite

The massive growth of online information obliged the availability of a thorough research in the domain of automatic text summarization within the Natural Language Processing (NLP) community. To reach this goal, different approaches should be integrated and collaborated. One of these approaches is the classification od documents. Therefore, the aim of this paper is to propose a successful framework for agricultural documents classification as a step forward for a language independent automatic summarization approach. The main target of our serial research is to propose a complete novel framework which not only responses to the question, but also gives the user an opportunity to find additional information that is related to the question. We implemented the proposed method. As a case study, the implemented method is applied on Arabic text in the agriculture field. The implemented approach succeeded in classifying the documents submitted by the user. The approach results have been evaluated using Recall, Precision and F-score measures.

show abstract

“…For assigning web page to category following formula is calculated for each document for every category [17]:…”

Section: Naïve Bayesian Classifiermentioning

confidence: 99%

Approach for Dimensionality Reduction in Web Page Classification

Sarode¹,

Gadge²

2014

IJCA

View full text Add to dashboard Cite

Dimensionality refers to number of terms in a web page. While classifying web pages high dimensionality of web pages causes problem. The main objective of reducing dimensionality of web pages is improving the performance of classifier. Processing time and accuracy are two parameters which influence the performance of a classifier. To reduce the processing time, less informative and redundant terms have to be removed from web pages.This research describes hybrid approach for dimensionality reduction in web page classification using a rough set and naïve Bayesian method. Feature selection and dimensionality reduction methods are used for reducing the dimensionality. Information gain method is used as feature selection method. Rough set based Quick Reduct algorithm is used for dimensionality reduction. Naïve Bayesian method is used for classifying web pages to optimal predefined categories. Assignment of web pages to category is based on maximum posterior probability. Words remaining after the process of feature selection and dimensionality reduction will be given to the classifier. Finally the classifier will assign most optimal predefined category to web pages.

show abstract

Text Document Preprocessing with the Bayes Formula for Classification Using the Support Vector Machine

Cited by 193 publications

References 8 publications

Sentiment Analysis of Tweets using SVM

Sentiment Analysis of Tweets using SVM

Arabic Documents classification method a Step towards Efficient Documents Summarization

Approach for Dimensionality Reduction in Web Page Classification

Contact Info

Product

Resources

About