Context-based sentiment analysis on customer reviews using machine learning linear models

Chinnalagu, Anandan; Durairaj, Ashok Kumar

doi:10.7717/peerj-cs.813

Cited by 12 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach is perfect as it can handle vast amounts of data and is automated. For sentiment prediction and optimization, ML algorithms including Naive Bayes (NB), DT, Regression, and Support Vector Machine (SVM) have typically been utilised to address the issue of sentiment classification on Twitter [5]. While SVM and Multinomial NB have been demonstrated to perform better in terms of accuracy and optimization.…”

Section: Methodsmentioning

confidence: 99%

SentiMLBench: Benchmark Evaluation of Machine Learning Algorithms for Sentiment Analysis

Yenkikar

Babu²

2023

IJEEI

View full text Add to dashboard Cite

Sentiment Analysis has been a topic of interest for researchers due to its increasing usage by Industry. To measure end-user sentiment., there is no clear verdict on which algorithms are better in real-time scenarios. A rigorous benchmark evaluation of various algorithms running across multiple datasets and different hardware architectures is required that can guide future researchers on potential advantages and limitations. In this paper, proposed SentiMLBench is a critical evaluation of key ML algorithms as standalone classifiers, a novel cascade feature selection (CFS) based ensemble technique in multiple benchmark environments each using a different twitter dataset and processing hardware. The best trained ensemble model with CFS enhancement surpasses current state-of-the-art models, according to experimental results. In a study, though ensemble model provides good accuracy, it falls short of neural networks accuracy by 2%. ML algorithms accuracy is poor as standalone classifiers across all three studies. The supremacy of neural networks is further stamped in study three where it outperforms other algorithms in accuracy by over 10%. Graphical processing unit provide speed and higher computational power at a fraction of a cost compared to a normal processor thereby providing critical architectural insights into developing a robust expert system for sentiment analysis.

show abstract

Section: Methodsmentioning

confidence: 99%

SentiMLBench: Benchmark Evaluation of Machine Learning Algorithms for Sentiment Analysis

Yenkikar

Babu²

2023

IJEEI

View full text Add to dashboard Cite

show abstract

“…SVM with hybrid features through inverse bias algorithm was used by Li & Xu (2014) to perform stock market sentiment analysis. Chinnalagu & Durairaj (2021) , Gui et al (2016) used SVM to recognize emotion and captured the cause information using convolution kernels from syntactic trees. In their sentiment analysis model, Li & Meesad (2016) used the k-means method to cluster the data into positive and negative groups, achieving a 70% accuracy.…”

Section: Related Literaturementioning

confidence: 99%

“… Mehta, Pandya & Kotecha (2021) developed and deployed a stock price prediction accuracy tool that considered public mood and used machine learning algorithms and Long Short-Term Memory to achieve the best accuracy of 92.45%. Chinnalagu & Durairaj (2021) suggested a high-performance yet cost-effective model that classified text and word embedding utilizing the fastText package from Facebook’s AI research (FAIR) Lab, as well as the standard Linear Support Vector Machine (LSVM). The accuracy of the fastText model achieved is 90.71%.…”

Section: Related Literaturementioning

confidence: 99%

Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble

Yenkikar

Babu

Hemanth

2022

PeerJ Computer Science

View full text Add to dashboard Cite

The exponential rise in social media via microblogging sites like Twitter has sparked curiosity in sentiment analysis that exploits user feedback towards a targeted product or service. Considering its significance in business intelligence and decision-making, numerous efforts have been made in this area. However, lack of dictionaries, unannotated data, large-scale unstructured data, and low accuracies have plagued these approaches. Also, sentiment classification through classifier ensemble has been underexplored in literature. In this article, we propose a Semantic Relational Machine Learning (SRML) model that automatically classifies the sentiment of tweets by using classifier ensemble and optimal features. The model employs the Cascaded Feature Selection (CFS) strategy, a novel statistical assessment approach based on Wilcoxon rank sum test, univariate logistic regression assisted significant predictor test and cross-correlation test. It further uses the efficacy of word2vec-based continuous bag-of-words and n-gram feature extraction in conjunction with SentiWordNet for finding optimal features for classification. We experiment on six public Twitter sentiment datasets, the STS-Gold dataset, the Obama-McCain Debate (OMD) dataset, the healthcare reform (HCR) dataset and the SemEval2017 Task 4A, 4B and 4C on a heterogeneous classifier ensemble comprising fourteen individual classifiers from different paradigms. Results from the experimental study indicate that CFS supports in attaining a higher classification accuracy with up to 50% lesser features compared to count vectorizer approach. In Intra-model performance assessment, the Artificial Neural Network-Gradient Descent (ANN-GD) classifier performs comparatively better than other individual classifiers, but the Best Trained Ensemble (BTE) strategy outperforms on all metrics. In inter-model performance assessment with existing state-of-the-art systems, the proposed model achieved higher accuracy and outperforms more accomplished models employing quantum-inspired sentiment representation (QSR), transformer-based methods like BERT, BERTweet, RoBERTa and ensemble techniques. The research thus provides critical insights into implementing similar strategy into building more generic and robust expert system for sentiment analysis that can be leveraged across industries.

show abstract

“…It deciphers positive, negative or neutral emotions through lexica and keyword enhancers [36]. It is commonly used in business to establish consumer trends, such as through customer reviews [37,38]. It has been previously used in healthcare to analyze sentiments surrounding patient experience [39] and COVID-19 vaccine hesitancy [40,41].…”

Section: Sentiment Analysis Of Personal Experiencementioning

confidence: 99%

Patient and family experience in pediatric spine surgery: a social media analysis

Levett

Elkaim

Weber

et al. 2022

Preprint

View full text Add to dashboard Cite

There is little data on patient and caregiver perceptions of spine surgery in children and youth. This study aims to characterize the personal experiences of patients, caregivers, and family members surrounding pediatric spine surgery through a qualitative and quantitative social media analysis. The Twitter application programming interface was searched for keywords related to pediatric spine surgery from inception to March 2022. Relevant tweets and accounts were extracted and subsequently classified using thematic labels. Tweet metadata was collected to measure user engagement via multivariable regression. Sentiment analysis using Natural Language Processing was performed on all tweets with a focus on tweets discussing the personal experiences of patients and caregivers. 2424 tweets from 1847 individual accounts were retrieved for analysis. Patients and caregivers represented 1459 (79.0%) of all accounts. Posts discussed the personal experiences of patients and caregivers in 83.5% of tweets. Pediatric spine surgery research was discussed in a few posts (n=90, 3.7%). Within the personal experience category, 975 (48.17%) tweets were positive, 516 (25.49%) were negative, and 533 (26.34%) were neutral. Presence of a tag (beta: -6.1, 95% CI -9.7 to -2.5) and baseline follower count (beta<0.001, 95% CI <0.001 to <0.001) significantly affected tweet engagement negatively and positively, respectively. Patients and caregivers actively discuss topics related to pediatric spine surgery on Twitter. Posts discussing personal experience are most prevalent, while posts on research are scarce, unlike previous social media studies. Pediatric spine surgeons can leverage this dialogue to better understand the worries and needs of patients and their families.

show abstract

Context-based sentiment analysis on customer reviews using machine learning linear models

Cited by 12 publications

References 13 publications

SentiMLBench: Benchmark Evaluation of Machine Learning Algorithms for Sentiment Analysis

SentiMLBench: Benchmark Evaluation of Machine Learning Algorithms for Sentiment Analysis

Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble

Patient and family experience in pediatric spine surgery: a social media analysis

Contact Info

Product

Resources

About