An Extensive study of Sentiment Analysis tools and Binary Classification of tweets using Rapid Miner

Vyas, Vishal; Uma, V.

doi:10.1016/j.procs.2017.12.044

Cited by 33 publications

(30 citation statements)

References 3 publications

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“…The model was tested using 41 reviews, and the maximum achieved accuracy was 78.05% [8]. Similarly, one more study focused on ML method and used SVM with 79.08% accuracy, Decision Tree (DT) with 75.16%, and Naïve Bayes (NB) with 76.47% accuracy on 400+ tweets [4]. The rule-based approach was used on financial news articles dataset containing 200 rows and achieved 75.6% accuracy [5].…”

Section: Related Workmentioning

confidence: 99%

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A Comparison of Machine Learning Techniques for Sentiment Analysis

Qaiser¹

2021

TURCOMAT

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The availability of the data has increased tremendously due to the excess usage of social media platforms like Twitter and Facebook. Due to the abundant availability of data, scientists, businesses, educationalists and other people working under different roles have started using Sentiment Analysis (SA) to get in-depth knowledge about the sentiments of the people regarding any topic of interest. There are many techniques to implement SA, and one of them is Machine Learning (ML). This study is focused on the comparison of ancient ML methods such as Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and a modern method, i.e., Deep Learning (DL). The ML techniques are applied to a single dataset to compare their performance in terms of accuracy to understand how they perform against each other. The study found that DL performed the best with 96.41% accuracy followed by NB and SVM with 87.18% and 82.05% respectively. DT performed the poorest with 68.21% accuracy.

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Section: Related Workmentioning

confidence: 99%

“…[1], [2], [8]. There are multiple techniques to apply SA such as lexicon and rule-based and Machine Learning (ML) as seen by [4], [8]- [12] but ML has great potential hence this study is focused on ML approach. The paper's organization is as follows: Section 2 discusses the related work in SA.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Techniques for Sentiment Analysis

Qaiser¹

2021

TURCOMAT

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“…A workflow suitable for text mining is illustrated in Fig. 11: the proposed Rapid Miner workflow implements a Support Vector Machine (SVM) algorithm [33]. The workflow of Fig.…”

Section: Web Mining: Analysis Of Social Trendsmentioning

confidence: 99%

Business Intelligence Improved by Data Mining Algorithms and Big Data Systems: An Overview of Different Tools Applied in Industrial Research

Massaro¹,

Vitti²,

Galiano³

et al. 2019

csit

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“…Vishal Vyas and V.Uma (2018) conducted experiments with Rapid Miner to analyze the tweets of sentiments and compared the accuracy levels with twenty different tools. They pre-processed the data in five steps: converting document to lower case, tokenization, filter stopwords, filter the word based on length and stemmed the words using Porter stemming algorithm [11].Jin Ding, Hailong Sun at.al(2018) developed an entity level sentiment analysis tool called "SentiSW", which contains sentiment classification and entity recognition which can classify the comments. They adopted preprocessing steps such as removing useless features and reduced the noise through words removal, words replacing and Snowball stemming [12].Mariem NEJI at.al(2018) proposed a semantic method to compose LingWs to give the support to the users to select a valid composition.…”

Section: Related Workmentioning

confidence: 99%

The Unprejudiced Stemmer to Prevent Etymological Behavior of Stemmed Morphemes Of Social Media Corpora

Rao*,

2019

IJITEE

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Sentiment Analysis is an application of Natural Langue Processing to analyze social media corpora to extract insights of corpora. Sentiment analytical results are the real feedback of the customers, which enables the organizations and companies to take appropriate decision on their products and business policies. Stemming plays in-evitable and vital role in sentiment analysis. Stemming is one of the phase of preprocessing the social media corpora. Today most of the researches uses strong stemmers to identify stem words of social media corpora. The most popular stemming algorithms such as Lancaster and Porter stemming algorithms causes prejudiced the meaning of the words. The over-stemmed words mislead the sentiment classification process. To prevent the over-stemming the Unprejudiced lighter stemming algorithm is proposed to sustain the meaning of the stemmed words. The propose Un-prejudiced algorithm uses lexical database and Parts of speech of Python Natural Language Tool Kit. There are a few stemming algorithm accuracy evaluation methods, in this paper we focused on Paice Error-rate relative to truncation (ERRT) measure to evaluate the accuracy of Lancaster, Porter and Unprejudiced stemming algorithms. The experiments were conducted on 25,758 source words and results were evaluated using Paice stem evaluation method and Sirsat method. The Paice Evaluation ERRT values 0.47209, 0.28703, 0.15502 of Lancaster, Porter, Unprejudiced respectively are proved that the Unprejudiced stemmer is more accurate than Lancaster and Porter. Sirsat’s stem evaluation method Average Words Conflation Factor (AWCF) results 10310.31, 14031.17, 23349.87 of Lancaster, Porter, Unprejudiced respectively are also proved the Unprejudiced stemming algorithm is more accurate than Lancaster and Porter stemming algorithms.

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An Extensive study of Sentiment Analysis tools and Binary Classification of tweets using Rapid Miner

Cited by 33 publications

References 3 publications

A Comparison of Machine Learning Techniques for Sentiment Analysis

A Comparison of Machine Learning Techniques for Sentiment Analysis

Business Intelligence Improved by Data Mining Algorithms and Big Data Systems: An Overview of Different Tools Applied in Industrial Research

The Unprejudiced Stemmer to Prevent Etymological Behavior of Stemmed Morphemes Of Social Media Corpora

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