Preprocessing and Feature Selection Approach for Efficient Sentiment Analysis on Product Reviews

Ghosh, Monalisa; Sanyal, Gautam

doi:10.1007/978-981-10-3153-3_72

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…• The removal of stop words: These were the highly repeated words in the text such as "the", "and", "but", "or", etc. Eliminating these words from the text reduced the dimensionality of the data, and helped build a more robust and efficient classification model [50]. This step involved eliminating some common and exclusive words; • Stemming: This step included using different stemmers such as snowball, Lovins, and Dawson [51] to convert all the derivative words back to their basic roots.…”

Section: Data Preprocessing and Feature Extractionmentioning

confidence: 99%

An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

et al. 2021

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As the COVID-19 pandemic rapidly spreads across the world, regrettably, misinformation and fake news related to COVID-19 have also spread remarkably. Such misinformation has confused people. To be able to detect such COVID-19 misinformation, an effective detection method should be applied to obtain more accurate information. This will help people and researchers easily differentiate between true and fake news. The objective of this research was to introduce an enhanced evolutionary detection approach to obtain better results compared with the previous approaches. The proposed approach aimed to reduce the number of symmetrical features and obtain a high accuracy after implementing three wrapper feature selections for evolutionary classifications using particle swarm optimization (PSO), the genetic algorithm (GA), and the salp swarm algorithm (SSA). The experiments were conducted on one of the popular datasets called the Koirala dataset. Based on the obtained prediction results, the proposed model revealed an optimistic and superior predictability performance with a high accuracy (75.4%) and reduced the number of features to 303. In addition, by comparison with other state-of-the-art classifiers, our results showed that the proposed detection method with the genetic algorithm model outperformed other classifiers in the accuracy.

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Section: Data Preprocessing and Feature Extractionmentioning

confidence: 99%

An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

et al. 2021

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“…To filter the selected aspect, stemming is applied so that aspects in plural form can be converted into singular form. Ghosh & Sanyal (2017) performed aspect level sentiment analysis selection using NB and SVM. The proposed method considered frequency of aspect and static measure called Term Frequency Inverse Document Frequency.…”

Section: Sentiment Analysismentioning

confidence: 99%

Aspect-based classification of product reviews using Hadoop framework

Rodrigues¹,

Chiplunkar²,

Fernandes³

2020

Cogent Engineering

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The advancement of e-commerce along with the quick development of product review discussion in the most recent decade, an enormous measure of sentiment data or reviews are produced which made it practically difficult for a customer to take an educated buy choice. A good number of customers share their conclusions about the product during these days. These reviews have an important role in customers' purchase-decision process. For a popular product there may be hundreds or thousands of reviews. This is difficult for a potential customer to go to each of them to make an educated choice on whether to buy the product or not. Also, this is difficult for manufacturer of the product to follow-up and to manage client opinions. In this scenario, the aspects-based sentimental analysis helps in analyzing the reviews and categorizing them into appropriate aspects. Aspect or feature refers to attributes or qualities of a product. The proposed work begins with collecting reviews from online shopping websites, identifying aspects and classifying opinion orientation of aspects with different sentiment analysis techniques using Hadoop framework. This paper proposes a new pattern-based method for aspect extraction and sentiment analysis which gives an accuracy in the ABOUT THE AUTHORS

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“…They achieved the higher accuracy with SVM was 75% in comparison with NB (65%) by using evaluation matrices precision and recall. A huge number of research papers with different ML classifiers namely Naive Bayes (NB) [11,21] Support Vector Machine (SVM) [10,[22][23][24], maximum entropy [17,25,26], decision trees [9,21,27] have been used mostly to build classification model in different domain (Table 1).…”

Section: Related Workmentioning

confidence: 99%

An ensemble approach to stabilize the features for multi-domain sentiment analysis using supervised machine learning

Ghosh

Sanyal

2018

J Big Data

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An opinion is a viewpoint or judgment about a specific thing that acts as a key influence on an individual process of decision making. People's belief and the choices they make are always dependent on how others see and evaluate the world. So opinion holds high values in many aspect of life. Sentiment analysis is the process of determining opinions or sentiments in textual documents as positive, or negative. In recent years, this field is widely appreciated by researchers due to its dynamic range of application in various numbers of fields. There are several areas such as marketing; politics; news analytics etc. which are benefited from the result of sentiment analysis. Due to the vast range of movies these days, it has become difficult for the audience to select their preferred genre of movie. Movie reviews turn out to be very useful

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Preprocessing and Feature Selection Approach for Efficient Sentiment Analysis on Product Reviews

Cited by 13 publications

References 14 publications

An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

Aspect-based classification of product reviews using Hadoop framework

An ensemble approach to stabilize the features for multi-domain sentiment analysis using supervised machine learning

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