Fuzzy Interpretation of Word Polarity Scores for Unsupervised Sentiment Analysis

Vashishtha, Srishti; Susan, Seba

doi:10.1109/icccnt49239.2020.9225646

Cited by 21 publications

(4 citation statements)

References 32 publications

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“…SSentiA (SVM) 77% 0.77 0.77 0.77 (13) Hybrid-SVM Hybrid LR 92% 92% ----0.91 0.90 (14) Fuzzy Logic 85% 0.95 0.91 0.93 Proposed SVMS 96% 0.96 0.98 0.97…”

Section: Proposed Model Against Existing Methodsmentioning

confidence: 99%

Aspect Based Sentiment Analysis and Opinion Mining on Twitter Data Set Using Linguistic Rules

Ahmad¹,

Shaikh²,

Tanwani³

2023

IJST

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Objectives: To propose the most effective machine learning technique for aspect based sentiment analysis of consumer feedback reviews by correlating people's emotions of different aspects of a product or service with word importance, linguistic rules, tagging, dependency relations and filtering to enhance the classification accuracy and to explore the different machine learning approaches in order to determine their significance and to spark interest in this field of study. Methods: The dataset has been taken from the most popular networking site twitter using twitter API. A total of 33,036 tweets were retrieved for this study from16 July 2022 to 16 Sept. 2022, but were only able to include 7570 tweets after data cleaning and then proposes a model named SVMS (SVM using Spacy) for sentiment Analysis. To measure the performance of a particular model, numerous analyses are conducted on the dataset and then the effectiveness of proposed model is compared with 4 basic classifiers LR, NB, RF and SVM. The parameters used in this study are accuracy, precision, recall, and F1 score. Findings: It is found that the Proposed SVMS model shows better results than all other algorithms by having 96% accuracy which is 1% faster than LR, 2% faster than NB, 5% faster than RF, and 4% faster than SVM, 96% precision, 98% recall and 97% F1 score. SVMS is recommended in this research context for the classification of the text and has a great significance for future researchers in sentences and text classification. Novelty: In this paper the performance of sentiment analysis classifiers is evaluated. In addition to general opinions, the work examines specific aspects such as product, quality, service, price, and ease of use. Comparing the proposed model with the existing models, it has been found that it outperformed the other models in most of the aspects.

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“…SSentiA (SVM) 77% 0.77 0.77 0.77 (13) Hybrid-SVM Hybrid LR 92% 92% ----0.91 0.90 (14) Fuzzy Logic 85% 0.95 0.91 0.93 Proposed SVMS 96% 0.96 0.98 0.97…”

Section: Proposed Model Against Existing Methodsmentioning

confidence: 99%

Aspect Based Sentiment Analysis and Opinion Mining on Twitter Data Set Using Linguistic Rules

Ahmad¹,

Shaikh²,

Tanwani³

2023

IJST

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“…It comprises over 3,300 words, each of which has a polarity score within the range from −5 (highest score for negative sentiment) to 5 (highest score for positive sentiment). Prior studies (Kim and Chung, 2020; Tan and Guan, 2021; Vashishtha and Susan, 2020) have relied on the AFINN lexicon, which is arguably one of the most basic and widely used lexicons for SA. Vashishtha and Susan (2020) determined that the AFINN lexicon has the greatest accuracy after evaluating the results of the fuzzy method on three benchmark data sets: hotel reviews data set, polarity movie data set by Pang and Lee (2008) and internet movie database (IMDb) data set.…”

Section: Methodsmentioning

confidence: 99%

“…Prior studies (Kim and Chung, 2020; Tan and Guan, 2021; Vashishtha and Susan, 2020) have relied on the AFINN lexicon, which is arguably one of the most basic and widely used lexicons for SA. Vashishtha and Susan (2020) determined that the AFINN lexicon has the greatest accuracy after evaluating the results of the fuzzy method on three benchmark data sets: hotel reviews data set, polarity movie data set by Pang and Lee (2008) and internet movie database (IMDb) data set. In addition, one of the present study’s goals is to classify customers’ feelings into three categories: positive, negative and neutral.…”

Section: Methodsmentioning

confidence: 99%

Customer sentiment analysis and prediction of halal restaurants using machine learning approaches

Hossain

Rahman

Uddin

et al. 2022

JIMA

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Purpose There is a strong prerequisite for organizations to analyze customer review behavior to evaluate the competitive business environment. The purpose of this study is to analyze and predict customer reviews of halal restaurants using machine learning (ML) approaches. Design/methodology/approach The authors collected customer review data from the Yelp website. The authors filtered the reviews of only halal restaurants from the original data set. Following cleaning, the filtered review texts were classified as positive, neutral or negative sentiments, and those sentiments were scored using the AFINN and VADER sentiment algorithms. Also, the current study applies four machine learning methods to classify each review toward halal restaurants into its sentiment class. Findings The experiment showed that most of the customer reviews toward halal restaurants were positive. The authors also discovered that all of the methods (decision tree, linear support vector machine, logistic regression and random forest classifier) can correctly classify the review text into sentiment class, but logistic regression outperforms the others in terms of accuracy. Practical implications The results facilitate halal restaurateurs in identifying customer review behavior. Social implications Sentiment and emotions, according to appraisal theory, form the basis for all interactions, facilitating cognitive functions and supporting prospective customers in making sense of experiences. Emotion theory also describes human affective states that determine motives and actions. The study looks at how potential customers might react to a halal restaurant’s consensus on social media based on reviewers’ opinions of halal restaurants because emotions can be conveyed through reviews. Originality/value This study applies machine learning approaches to analyze and predict customer sentiment based on the review texts toward halal restaurants.

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“…These applications customarily use organized informa-tion which is hard to produce and restricted in amount [5]. Then the data produced by client audits dont have that much impediments in decision making for structured data [6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Elephant Herd Random Forest Machine Learning Approach To Estimate the Sentiment Value of Online Customer Review

Sanshi¹,

Krishna²,

Vatambeti³

2023

IJCDS

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Nowadays, big data has attracted the attention of whole advanced world with its applications and features. Machine learning (ML) models are used to perform the online administrations in good manner .The ML approaches turned into a moving field in analyzing enormous data; consequently the accomplishment of online administrations or business depends on the client audits. Nearly the online customer review contains positive, negative and neutral sentiment value. In marketing system and product development fields the sentiment analysis value prediction has important role. In this paper, a novel Elephant Herd Random Forest Machine Learning (EHRFML) methodology is proposed to compute the sentiment value of online customer review. Moreover, customer review datasets are preprocessed and unwanted information is removed using machine learning approach. Sequentially, the outcomes of proposed system are compared with existing technique using parameters like accuracy, precision, recall, aspect term specification and opinion condition obtained good results by getting high accuracy based on opinion specification.

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Fuzzy Interpretation of Word Polarity Scores for Unsupervised Sentiment Analysis

Cited by 21 publications

References 32 publications

Aspect Based Sentiment Analysis and Opinion Mining on Twitter Data Set Using Linguistic Rules

Aspect Based Sentiment Analysis and Opinion Mining on Twitter Data Set Using Linguistic Rules

Customer sentiment analysis and prediction of halal restaurants using machine learning approaches

A Novel Elephant Herd Random Forest Machine Learning Approach To Estimate the Sentiment Value of Online Customer Review

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