The effects of pre-processing strategies in sentiment analysis of online movie reviews

Zin, Harnani Mat; Mustapha, Norwati; Murad, Masrah Azrifah Azmi; Sharef, Nurfadhlina Mohd

doi:10.1063/1.5005422

Cited by 17 publications

(7 citation statements)

References 19 publications

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“…Then cleaning is used for delimiter or deletion of characters or punctuation marks and URLs and emoticons [20]. Tokenizing is a process to make a sentence more meaningful by breaking the sentence into words [21]. Only take words that have an important meaning in the training data [22].…”

Section: Methodsmentioning

confidence: 99%

Sentiment Analysis to analyze Vaccine Enthusiasm in Indonesia on Twitter Social Media

Rahmaddeni¹,

Firdaus

Anam³

et al. 2021

JAIA

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Vaccines are one way to prevent the coronavirus from entering the human body, although it is not 100% accurate. However, the implementation of vaccination in Indonesia is still controversial. People give their opinions directly or through social media such as Twitter. Retrieval of tweets using the Twitter API and using python. The data obtained is then preprocessed using case folding, cleaning, tokenizing, filtering, and stemming. After that, the model was evaluated using the Naive Bayes method. Naïve Bayes is a classification method that can predict the probability of a class to produce decisions based on learning data. Currently, nave Bayes is one of the methods to find accuracy in sentiment analysis that is often used and is the best. The results of this study obtained an accuracy of 79%.

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Section: Methodsmentioning

confidence: 99%

Sentiment Analysis to analyze Vaccine Enthusiasm in Indonesia on Twitter Social Media

Rahmaddeni¹,

Firdaus

Anam³

et al. 2021

JAIA

View full text Add to dashboard Cite

show abstract

“…According to the results, using appropriate feature selection and representation of the dataset may increase the classification accuracy in sentiment analysis such as 1-to-3 grams perform better than other representations and feature extraction. Zin et al [12] showed the effects of various preprocessing strategies such as stopwords, numbers, and punctuations with experimental results on online movie reviews. Their study proved that preprocessing affects the performance of the classification in a good way especially on the SVM with nonlinear kernel.…”

Section: Related Workmentioning

confidence: 99%

The impact of text preprocessing on the prediction of review ratings

Işik¹,

Dağ²

2020

Turk J Elec Eng & Comp Sci

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With the increase of e-commerce platforms and online applications, businessmen are looking to have a rating and review system through which they can easily reveal the feelings of customers related to their products and services. It is undeniable from the statistics that online ratings and reviews attract new customers as well as increase sales by means of providing confidence, ratification, opinions, comparisons, merchant credibility, etc. Although considerable research has been devoted to the sentiment analysis for review classification, rather less attention has been paid to the text preprocessing which is a crucial step in opinion mining especially if convenient preprocessing strategies are found out to increase the classification accuracy. In this paper, we concentrate on the impact of simple text preprocessing decisions in order to predict fine-grained review rating stars whereas the majority of previous work focused on the binary distinction of positive vs. negative. Therefore, the aim of this research is to analyze preprocessing techniques and their influence, at the same time explain the interesting observations and results on the performance of a five-class-based review rating classifier.

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“…Before feature selection, various pre-processing steps are performed to minimize the noise from social media text [24]. The steps are:…”

Section: A Data Pre-processingmentioning

confidence: 99%

Sentiment Analysis for Social Media using SVM Classifier of Machine Learning

2019

IJITEE

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Sentiment analysis is an area of natural language processing (NLP) and machine learning where the text is to be categorized into predefined classes i.e. positive and negative. As the field of internet and social media, both are increasing day by day, the product of these two nowadays is having many more feedbacks from the customer than before. Text generated through social media, blogs, post, review on any product, etc. has become the bested suited cases for consumer sentiment, providing a best-suited idea for that particular product. Features are an important source for the classification task as more the features are optimized, the more accurate are results. Therefore, this research paper proposes a hybrid feature selection which is a combination of Particle swarm optimization (PSO) and cuckoo search. Due to the subjective nature of social media reviews, hybrid feature selection technique outperforms the traditional technique. The performance factors like f-measure, recall, precision, and accuracy tested on twitter dataset using Support Vector Machine (SVM) classifier and compared with convolution neural network. Experimental results of this paper on the basis of different parameters show that the proposed work outperforms the existing work

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The effects of pre-processing strategies in sentiment analysis of online movie reviews

Cited by 17 publications

References 19 publications

Sentiment Analysis to analyze Vaccine Enthusiasm in Indonesia on Twitter Social Media

Sentiment Analysis to analyze Vaccine Enthusiasm in Indonesia on Twitter Social Media

The impact of text preprocessing on the prediction of review ratings

Sentiment Analysis for Social Media using SVM Classifier of Machine Learning

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