Sentiment Classification based on Domain Prediction

Pak, Muhammet Yasin; Günal, Serkan

doi:10.5755/j01.eie.22.2.14599

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Text classification, which can be simply defined as assigning documents into predefined categories according to their content, has gained importance due to the increase in textual data [1]. Text classification can be used to solve miscellaneous problems such as spam e-mail filtering [2,3], SMS spam filtering [4,5], topic detection [6,7], author identification [8,9], language identification [10,11], web page classification [12,13], sentiment analysis [14,15] and medical document classification [16,17].…”

Section: Introductionmentioning

confidence: 99%

On classification of abstracts obtained from medical journals

Parlak

Uysal

2019

Journal of Information Science

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Classification of medical documents was mostly carried out on English data sets and these studies were performed on hospital records rather than academic texts. The main reasons behind this situation are the lack of publicly available data sets and the tasks being costly and time-consuming. As the first contribution of this study, two data sets including Turkish and English counterparts of the same abstracts published in Turkish medical journals were constructed. Turkish is one of the widely used agglutinative languages worldwide and English is a good example of non-agglutinative languages. While English abstracts were obtained automatically from MEDLINE database with a computer program, Turkish counterparts of these documents were collected manually from the Internet. As the second contribution of this study, an extensive comparison on classification of abstracts obtained from Turkish medical journals was made by using these two equivalent data sets. Features were extracted from text documents with three different approaches: unigram, bigram and hybrid. Hybrid approach includes a combination of unigram and bigram features. In the experiments, three different feature selection methods and seven different classifiers were utilised. According to the results on both data sets, classification performance of the English abstracts outperformed the Turkish counterparts. Maximum accuracies were obtained from the combination of unigram features, distinguishing feature selector (DFS) and multinomial naïve Bayes (MNB) classifier for both data sets. Unigram features were generally more efficient than bigram and hybrid features. However, analysis of top-10 features indicated that nearly half of the features were translations of each other for Turkish and English data sets.

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Section: Introductionmentioning

confidence: 99%

On classification of abstracts obtained from medical journals

Parlak

Uysal

2019

Journal of Information Science

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“…Text classification is assigning documents to predefined classes. Web page classification [1,2], sentiment classification [3][4][5], customer complaints classification [6,7], spam detection [8,9], tweet classification [10,11] and other classifications [6,[12][13][14][15][16][17][18] are samples of text classification in digital environment.…”

Section: Introductionmentioning

confidence: 99%

Effects of Preprocessing on Text Classification in Balanced and Imbalanced Datasets

2024

KSII TIIS

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In this study, preprocessings with all combinations were examined in terms of the effects on decreasing word number, shortening the duration of the process and the classification success in balanced and imbalanced datasets which were unbalanced in different ratios. The decreases in the word number and the processing time provided by preprocessings were interrelated. It was seen that more successful classifications were made with Turkish datasets and English datasets were affected more from the situation of whether the dataset is balanced or not. It was found out that the incorrect classifications, which are in the classes having few documents in highly imbalanced datasets, were made by assigning to the class close to the related class in terms of topic in Turkish datasets and to the class which have many documents in English datasets. In terms of average scores, the highest classification was obtained in Turkish datasets as follows: with not applying lowercase, applying stemming and removing stop words, and in English datasets as follows: with applying lowercase and stemming, removing stop words. Applying stemming was the most important preprocessing method which increases the success in Turkish datasets, whereas removing stop words in English datasets. The maximum scores revealed that feature selection, feature size and classifier are more effective than preprocessing in classification success. It was concluded that preprocessing is necessary for text classification because it shortens the processing time and can achieve high classification success, a preprocessing method does not have the same effect in all languages, and different preprocessing methods are more successful for different languages.

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“…On the other hand, in word n-gram approach, an n-gram is a contiguous sequence of n words from a given text after the text is tokenized into the words. Word n-gram approach is often referred to the bag-of-words model, probably the most common approach in text classification [10][11][12][13].…”

Section: Text Representationmentioning

confidence: 99%

The Impact of Text Representation and Preprocessing on Author Identification

Pak¹,

Günal²

2017

ANADOLU UNIVERSITY JOURNAL OF SCIENCE AND TECHNOLOGY a - Applied Sciences and Engineering

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Author identification, one of the popular topics in text classification and natural language processing, basically aims to determine the author of a given text through various analyses. In the literature, different text representation approaches and use of preprocessing steps are considered for author identification problem. This paper aims to comprehensively examine the impact of text representation and preprocessing steps on author identification specifically for Turkish language. For this purpose, the contributions of all possible combinations of different text representation approaches, namely unigram and bigram, together with the preprocessing tasks, including stemming and stop-word removal, to the performance of author identification are investigated. For the experimental evaluation, a brand new dataset is constituted. Also, two different classification algorithms, namely Multinomial Naive Bayes and Sequential Minimal Optimization, are employed. The results of the experimental analysis reveal that using bigram features alone should be avoided. Besides, it is shown that stop-words should be kept inside the text while stemming can be preferred depending on the classification algorithm so that higher performance can be achieved for author identification.

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Sentiment Classification based on Domain Prediction

Cited by 3 publications

References 11 publications

On classification of abstracts obtained from medical journals

On classification of abstracts obtained from medical journals

Effects of Preprocessing on Text Classification in Balanced and Imbalanced Datasets

The Impact of Text Representation and Preprocessing on Author Identification

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