Effect of Negation in Sentences on Sentiment Analysis and Polarity Detection

Mukherjee, Partha; Badr, Youakim; Doppalapudi, Shreyesh; Srinivasan, Satish; Sangwan, Raghvinder S.; Sharma, Rahul

doi:10.1016/j.procs.2021.05.038

Cited by 62 publications

(29 citation statements)

References 27 publications

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“…Thirdly, the lexicons compared show there is need for lexical algorithms to consider negation. This is consistent with the findings of studies like Mukherjee et al 2021;Gupta & Joshi 2021;Singh 2021;Garg & Subrahmanyam, 2021 that stated negation handling improves sentiment classification perf ormance. Lastly, the finding shows there is need to create wordlist for non-English text where needed as this improves the performance of SA lexicons.…”

Section: Chapter Summarysupporting

confidence: 91%

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

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Banking is an essential component of our day-to-day activity, and the sector contributes to the development of every Nation's economy. To ensure bank stability, the banks have gone through several deregulation and reformation processes. Unfortunately, these processes and technological advancement have led to increased competitiveness, saturated market, and low profitability. Thus, the need for banks to build customer centric service to gain profitability and stability is vital. This opens the need to investigate customers attitude towards banking.With the growing usage of social network sites (SNS), the user generated content (UGC) has opened opportunities for banks to mine customers opinion . This can help the banks to generate insight into their product and services, create marketing strategies and manage their reputation. To the customers, this serves as source to information that can help in supporting decision making on product or service purchase. In this study, sentiment analysis (SA) techniques were employed to investigate customers attitude towards banking us ing Twitter data. However, the unstructured nature of the data and word ambiguity made sentiment analysis complicated. In the context of this study, SA is more difficult because it involves the natural language processing of Pidgin English and English words in the bank domain.Unfortunately, there are limited or no resources for this purpose.

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Section: Chapter Summarysupporting

confidence: 91%

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

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“…Similar findings were reported for Naïve Bayes, Artificial Neural Network (ANN), and Recurrent Neural Network (RNN) models used for sentiment analysis. The largest positive effect of negation tagging was observed in RNN models [8]. Kaddoura et al demonstrated that treating negations in Facebook posts resulted in 20% increase in F1-score in sentiment analysis [9].…”

Section: Related Researchmentioning

confidence: 99%

The Influence of NegEx on ICD-10 Code Prediction in Swedish: How is the Performance of BERT and SVM Models Affected by Negations?

Budrionis¹,

Chomutare²,

Svenning³

et al. 2022

Linköping Electronic Conference Proceedings

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“…Mukherjee et al [13] presented an end-to-end Sentiment Analysis (SA) method for Handling Negation, and Negation Scope Marking and Negation Identification. The method presented a Customized Negation Marking Approach for performing experiments on Sentiment Analysis and explicit Negation Detection with Distinct Machine Learning (ML) approaches like SVM, Naive Bayes (NB), Recurrent Neural Network (RNN), and Artificial Neural Network (ANN) on Sentiment Analysis (SA) of Amazon Review.…”

Section: Related Workmentioning

confidence: 99%

Twitter Text Sentiment Analysis of Amazon Unlocked Mobile Reviews Using Supervised Learning Techniques

Bharathi¹,

R.²,

R.³

2022

INDJCSE

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Due to the drastic increase in usage of Internet and e-commerce, online shopping becomes very common in day to day lives. One of the popular e-commerce websites is Amazon, which allows users to post Reviews and Ratings regarding the purchased products. It finds useful for other customers and companies to improve the product quality. Since the manual extraction of sentiments from User Product Reviews is a tedious process, accurate and Automated Sentiment Analysis (SA) tools are essential. This research work aims to examine the performance of different Machine Learning (ML) models in analyzing the sentiments of Amazon Product Reviews. Initially, the Product Reviews are pre-processed in different ways to transform the data into a useful format. Besides, Term Frequency -Inverse Document Frequency (TF-IDF) vectorizer is used to derive Feature Embeddings. Finally, three Machine Learning (ML) models namely Gaussian Naive Bayes (GNB), Logistic Regression (LR), and Support Vector Machines (SVM) are used for Sentiment Analysis (SA). The Performance Validation of the Machine Learning (ML) models is performed using Benchmark Dataset from Kaggle Repository. The Experimental Results reported that the Support Vector Machine (SVM) model has resulted in better Performance over the other Machine Learning (ML) models interms of Different Measures.

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Effect of Negation in Sentences on Sentiment Analysis and Polarity Detection

Cited by 62 publications

References 27 publications

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Statistical learning approaches to sentiment analysis in the Nigerian banking context

The Influence of NegEx on ICD-10 Code Prediction in Swedish: How is the Performance of BERT and SVM Models Affected by Negations?

Twitter Text Sentiment Analysis of Amazon Unlocked Mobile Reviews Using Supervised Learning Techniques

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