Customer Satisfaction Measurement using Sentiment Analysis

Al-Otaibi, Shaha; Alnassar, Allulo; Alshahrani, Asma M.; Al-Mubarak, Amany; Albugami, Sara; Almutiri, Nada; Albugami, Aisha

doi:10.14569/ijacsa.2018.090216

Cited by 38 publications

(26 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The current findings are in line with earlier works, where it is proved that longer reviews are often associated with better product performances (Ghasemaghaei et al, 2018), and the sentiment scores may be used as a proxy to measure the satisfaction (Al-Otaibi et al, 2018).…”

Section: Resultssupporting

confidence: 92%

“…We used R-Studio and plugins like tm, wordcloud, wordcloud2 and the command "gsub" to pre-process the text data. In line with earlier studies, review texts are treated for data cleaning process (Al-Otaibi et al, 2018;Gaikar and Marakarkandy, 2015). This process includes removal of punctuations/special characters/numbers/symbols to lower-casing the words, removal of stem words and blank spaces.…”

Section: Pre-processing the Datamentioning

confidence: 99%

See 1 more Smart Citation

Sentiment analysis of consumer reviews – a comparison of organic and regular food products usage

Rajeswari

Madhavan

Venkatesakumar

et al. 2020

RAMJ

View full text Add to dashboard Cite

Purpose This study aims to compare online review characteristics, review length and review sentiment score between “organic” and “regular” food products. In addition, variations in the consumer sentiment scores across the review lengths are studied. Design/methodology/approach This study fits into the descriptive research design. From Amazon’s website, the consumer product reviews are scrapped. Using the text analytical package “sentiment” in R-Studio, we computed the sentiment scores and counted the number of words in each review. The mean sentiment scores and mean review length are compared for regular and organic products using one-way ANOVA. Sentiment score variation across review length and product class is studied through factorial ANOVA. Sample reviews of ghee and honey are used to test the hypotheses. Findings The review length shows a significant difference between the regular and organic products. The mean number of words in the regular products reviews is significantly lower than the mean number of words in the organic product reviews. The regular products’ mean sentiment score is significantly lower than the mean sentiment score of organic products. The mean sentiment scores are not consistent between ghee and honey. Sentiment scores are better for organic honey and regular ghee products. For regular ghee products, longer reviews result in lower sentiment scores. For regular and organic versions of honey, longer reviews are associated with better sentiment scores. Research limitations/implications This study did not include the helpfulness of a review and the demographic data of the reviewers. Practical implications Sentiment scores’ variations across the regular and organic and product categories such as ghee and honey give a comprehensive feedback to the firms. It also indirectly communicates a brand’s evaluation by the consumers and the performance feedback for an upward extension like the organic category. Social implications Studies on organic category give feedback for environment-friendly products and consumer attitude shift towards safer products. Originality/value Very limited studies have reported the upward line extensions. The authors study the upward line extension organic and associated sentiment scores variation. The role of review length and its systematic influence on the sentiment scores, variations in the review due to the product nature (organic/regular) are unique contributions of this study.

show abstract

Section: Resultssupporting

confidence: 92%

Section: Pre-processing the Datamentioning

confidence: 99%

Sentiment analysis of consumer reviews – a comparison of organic and regular food products usage

Rajeswari

Madhavan

Venkatesakumar

et al. 2020

RAMJ

View full text Add to dashboard Cite

show abstract

“…Through the R-Programing and the R-Studio, we use plugins such as wordcloud, wordcloud2, tm and gsub command to preprocess the data. As per the earlier literature guidelines, the preprocessing is done (Al-Otaibi et al, 2018;Gaikar and Marakarkandy, 2015). In this stage, various tasks such as the removal of punctuations, special characters in a review, numbers and symbols, lowercasing the words and removal of stem words and blank spaces are carried out.…”

Section: Pre-processing Datamentioning

confidence: 99%

Distribution characteristics of star ratings in online consumer reviews

Venkatesakumar

Vijayakumar

Riasudeen

et al. 2020

XJM

View full text Add to dashboard Cite

Purpose The star rating summarises the review content and conveys the message faster than other review components. Star ratings influence helpfulness of the reviews, and extreme reviews are considered as less helpful in the decision process. However, literature has rarely addressed variations in star ratings across product categories and variations between two online retailers. In this paper, the authors have compared the distribution of star ratings across 11 products and among the retailers. Design/methodology/approach Online reviews for 11 product categories have collected, and the authors compared the distribution of star ratings across 11 products and retailers. Correspondence analysis has been applied to show the association between star ratings and product categories for the e-retail firms. Findings The Amazon site contains proportionately more number of 1-star rated reviews than Flipkart. In Amazon reviews, few product categories are closely associated with 1-star and 2-star reviews, whereas no product categories are closely associated with 1-star and 2-star reviews in Flipkart reviews. The results indicate two distinct communication strategies followed by the firms in managing online consumer reviews. Research limitations/implications The authors did not analyse data across demographic details because of access restriction policies of the websites. Practical implications Understanding the distribution of review characteristics will improve the consumer’s decision-making ability and using online review content judiciously. Social implications This study’s results show significant insights on online retailing by providing cues in using shopping sites and online review characteristics of two prominent retailers. Originality/value This paper has brought out a distinct distribution pattern of online review between Amazon and Flipkart. Amazon allows a higher degree of negative contents, whereas Flipkart allows more number of positive reviews.

show abstract

“…Social media analytics were used to analyze the customer opinions by following a hash tag on tweeting messages. Several feature extraction methods were analyzed to get the high accuracy [1].…”

Section: A Literature Reviewmentioning

confidence: 99%

Interrogation of Sentiment Perusing With Hash Counting Vectorizer and Term Inverse Frequency Transformer Using Machine Learning Classification

Rao*,

Devi

2019

IJRTE

View full text Add to dashboard Cite

With the fast growing technology, the business is moving towards increasing their profit by interpreting the customer satisfaction. The customer satisfaction can be analyzed in many ways. It is the responsibility of the business to analyze the customer satisfaction in order to improve their turnover and profit. With the current trend, the customers are giving their feedback through mobile and internet. With this overview, this paper attempts to analyze the sentiment of the customer feedback for the movie. The sentiment Analysis on movie Review dataset from the KAGGLE Machine learning repository is used for implementation. The type of sentiment classes is predicted through the following ways. Firstly, the sentiment count for each class is displayed and the top feature words for each sentiment class are also extracted from the dataset. Secondly, the dataset is sampled with counting vectorizer and then fitted with the classifiers like Logistic Regression Classifier, Linear SVM Classifier, Multinomial Naives Bayes Classifier, Gradient Boosting Classifer, Guassian Naive Bayes Classifier, Random Forest Classifier, Decision Tree Classifier and and Extra Tree Classifier. Thirdly, the dataset is sampled with Hashing vectorizer and then fitted with the above specified classifiers. Fourth, the dataset is sampled with TFIFD vectorizer and then fitted with the above specified classifiers. Fifth, the dataset is sampled with TFIFD Transformer and then fitted with the above specified classifiers. Sixth, the Performance analysis of classifiers is performed by analyzing the metrics like Precision, Recall, Fscore and Accuracy. The implementation is carried out using python code in Spyder Anaconda Navigator IP Console. Experimental results shows that the analysis of sentiment done by the random forest classifier is found to be more effective with the Accuracy of 89% for Counting vectorizer and TFIFD transformer, Accuracy of 87% for Hashing vectorizer and Accuracy of 88% for TFIFD vectorizer.

show abstract

Customer Satisfaction Measurement using Sentiment Analysis

Cited by 38 publications

References 5 publications

Sentiment analysis of consumer reviews – a comparison of organic and regular food products usage

Sentiment analysis of consumer reviews – a comparison of organic and regular food products usage

Distribution characteristics of star ratings in online consumer reviews

Interrogation of Sentiment Perusing With Hash Counting Vectorizer and Term Inverse Frequency Transformer Using Machine Learning Classification

Contact Info

Product

Resources

About