Comparative analysis of effect of stopwords removal on sentiment classification

Ghag, Kranti; Shah, Ketan

doi:10.1109/ic4.2015.7375527

Cited by 66 publications

(36 citation statements)

References 13 publications

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“…Classification of leprosy using Ridley-Jopling and WHO classification with its detailed information. [20] Comparison of Ridley-Jopling and WHO classification which is better in comparison with other clinical classification and their operational methods [19].Importance and methods of preprocessing in text mining by removal of stop words on an unstructured data to get better output [17]. Creating bags of words, considering combinations of cases, tokenizing and putting it in SVM trained model and measuring using classification parameters such as F1 score [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Clinical Text Mining of Electronic Health Records to Classify Leprosy Patients Cases

Mehta,

Dharamsey,

Domal

et al. 2020

IJITEE

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Leprosy is one of the major public health problems and listed among the neglected tropical diseases in India. It is also called Hansen's Diseases (HD), which is a long haul contamination by microorganisms Mycobacterium leprae or Mycobacterium lepromatosis.Untreated, leprosy can dynamic and changeless harm to the skin, nerves, appendages, and eyes. This paper intends to depict classification of leprosy cases from the main indication of side effects. Electronic Health Records (EHRs) of Leprosy Patients from verified sources have been generated. The clinical notes included in EHRs have been processed through Natural Language Processing Tools. In order to predict type of leprosy, Rule based classification method has been proposed in this paper. Further our approach is compared with various Machine Learning (ML) algorithms like Support Vector Machine (SVM), Logistic regression (LR) and performance parameters are compared.

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Section: Literature Reviewmentioning

confidence: 99%

Clinical Text Mining of Electronic Health Records to Classify Leprosy Patients Cases

Mehta,

Dharamsey,

Domal

et al. 2020

IJITEE

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“…This study uses Sastrawi Stemmer adapted from the Nazief-Andriani [18] algorithm with a modified confix-stripping [19]. i. Stopwords Removal is a word that often appears and does not have any meaning [9]. Stopwords in Indonesian such as "yang", "di", "untuk", and "dari".…”

Section: Preprocessingmentioning

confidence: 99%

“…Preprocessing techniques for text classification are stemming and stopwords removal [6] [7]. The "stemming" is turning a word into a root word by removing the phrase prefix [8], While the "stopwords removal" is removed words that often appear and do not have any meaning [9].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Stemming and Removal of Stopwords on the Accuracy of Sentiment Analysis on Indonesian-language Texts

Pradana¹,

Hayaty²

2019

KINETIK

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Preprocessing is an essential task for sentiment analysis since textual information carries a lot of noisy and unstructured data. Both stemming and stopword removal are pretty popular preprocessing techniques for text classification. However, the prior research gives different results concerning the influence of both methods toward accuracy on sentiment classification. Therefore, this paper conducts further investigations about the effect of stemming and stopword removal on Indonesian language sentiment analysis. Furthermore, we propose four preprocessing conditions which are with using both stemming and stopword removal, without using stemming, without using stopword removal, and without using both. Support Vector Machine was used for the classification algorithm and TF-IDF as a weighting scheme. The result was evaluated using confusion matrix and k-fold cross-validation methods. The experiments result show that all accuracy did not improve and tends to decrease when performing stemming or stopword removal scenarios. This work concludes that the application of stemming and stopword removal technique does not significantly affect the accuracy of sentiment analysis in Indonesian text documents.

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“…some of the authors mainly concentrated on stop word removal for achieving better accuracy. By using Ghag and Shah [1] observed data processing techniques on movie reviews for the effects of stop words removal .Accuracy on unprocessed dataset increased to removal stop words dataset by sentiment classifiers and display better than other classifier which is based on term weighting techniques.S. Rill et.…”

Section: Literature Surveymentioning

confidence: 99%

“…1. rt optionsnipper aapl beat on both eps and revenues sees q rev bb est b httpstcohfhxqjiob 2. rt optionsnipper aapl beat on both eps and revenues sees q rev bb est b httpstcohfhxqjiob 3. lets see this break all timers aapl 4. rt sylvacap things might get ugly for aapl with the iphone delay with aapl down that means almost all of the fang stocks were down posâ€¦ 5. aapl wow this was supposed to be a throwaway quarter and aapl beats by over million in revenue trillion dollar company by [1] RT @option_snipper: $AAPL beat on both eps and revenues. SEES 4Q REV.…”

Section: Removal Of Punctuation Marksmentioning

confidence: 99%

Word Cloud for Online Mobile Phone Tweets towards Sentiment Analysis

Venkatesh¹,

Kalaivani²

2019

IJEAT

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People became more eager to express and share their opinions on web regarding day-to-day activities or global issues. Social media contributed a transparent platform to share views across the world. Recently research communities, academia, public and service industries are working rigorously on sentiment analysis also termed as opinion mining, to extract and analyze public mood and views. Data pre-processing is a crucial step in sentiment analysis and selecting an appropriate pre-processing methods can improve classification accuracy. In this paper, we explore the role text pre-processing of online mobile phone reviews towards Sentiment Analysis. Proposed text pre-processing methods remove inconsistent and redundant elements on the collected data to improve classification accuracy. Proposed Preprocessing methods involves removal of punctuations, special characters, digits, escaping HTML characters, decoding data, Apostrophe Lookup, Removal of Stop-words, Removal of URLs, Removal of Expressions. The final pre-processed online review data are presented in the form of word cloud with the frequency statistics of the keywords.

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Comparative analysis of effect of stopwords removal on sentiment classification

Cited by 66 publications

References 13 publications

Clinical Text Mining of Electronic Health Records to Classify Leprosy Patients Cases

Clinical Text Mining of Electronic Health Records to Classify Leprosy Patients Cases

The Effect of Stemming and Removal of Stopwords on the Accuracy of Sentiment Analysis on Indonesian-language Texts

Word Cloud for Online Mobile Phone Tweets towards Sentiment Analysis

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