Sentiment Analysis on Online Product Reviews

Bose, Rajesh; Dey, Raktim Kumar; Roy, Sandip; Sarddar, Debabrata

doi:10.1007/978-981-13-7166-0_56

Cited by 25 publications

(15 citation statements)

References 13 publications

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“…The clustering technique consist of k-means. The decision tree technique consist of random forest technique (Akinkunmi, 2019) while the ruled-based classifiers consist of confidence criterion and support criterion as reported by (Bose et al, 2018). This research has produced a taxonomy which serves as a guide for the choice of techniques in sentiment analysis.…”

Section: Resultsmentioning

confidence: 99%

“…The classification level can be document, sentence, word and even aspect level (Archana & Kishore, 2017). In a study by (Bose et al, 2018), ruled-based is based on rules which can be further classified in to two(3) as confidence criterion and support criterion(see in Figure 5).…”

Section: 34mentioning

confidence: 99%

“…In a study by Bose et, al (2018) sentiment analysis can be expressed as a tool used to analyze opinions or moods on a specific matter-services, products or subject written in text or extracted from social media platforms, blog post, comments or web reviews and so on. Sentiments analysis deals classification of opinions or based on its polarity-label or bearing of the opinion according to the three(3) classes: negative, positive or neutral (Alkubaisi et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…is another taxonomy of sentiment analysis techniques developed by(Bose et al, 2018). The taxonomy categorized sentiment classification techniques in to three (3) namely: Machine learning, lexicon-based approach and hybrid approach or combination of both.…”

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confidence: 99%

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Sentiment Analysis Techniques and Application-Survey and Taxonomy

Umar

Bena

Wadata

2021

ETJ

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Nowadays, social media platforms, blogs, and e-commerce are commonly use to express opinion on politics, movies, products, education respectively; for election forecasting, business boosting and improvement of teaching and learning. As a result, data generation becomes easier; producing big data which requires appropriate techniques and tools to analyse easily, accurately and timely. Thus, making sentiment analysis very demanding research area. This study will investigate on what basis (sentiment classification level) or area of application (data source) do supervised machine learning approaches particularly Support Vector Machine (SVM), Naïve Bayes, and Maximum Entropy algorithms, and other technique-lexicon-based approach give the best result in sentiment analysis. Based on the review of the literature there is a contradiction on the point that SVM generated the best result in analyzing student sentiment on document level. This study also discovers that sentiment analysis differs from system to system based on polarity (types of the classes to predict: positive or negative, subjective or objective), different levels of classification (sentence, phrase, or document level) and language that is processed. This research produces a taxonomy which serves as a guide for the choice of techniques in sentiment analysis. The taxonomy explores the sentiment classification levels and data preprocessing stages. It also explores that sentiment analysis techniques were organised in to three (3) groups; Machine learning, Lexicon and hybrid or combination. The machine learning techniques were sub-grouped in to two (2) namely; supervised and unsupervised. The supervised were organized in to two (2): Classification and Regression. un-supervised machine learning techniques includes clustering and association. The clustering technique consist of k-means. Decision tree which is a classification based under supervised type of machine learning technique consist of random forest,(Akinkunmi, 2019) while the ruled-based classifiers consist of confidence criterion and support criterion. The commonly used tools are Weka, Python compiler, and R programming tool.

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Section: Resultsmentioning

confidence: 99%

Section: 34mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Sentiment Analysis Techniques and Application-Survey and Taxonomy

Umar

Bena

Wadata

2021

ETJ

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“…In particular, SVM, NB, DTs, RF, and LR methods, etc. which are used extensively with high accuracy in wide application fields that include sentiment analysis, such as cyberhate detection [19] movie and product reviews [20], [21], abusive language detection [22], cyberbullying identification [23], and social media [24]. In addition to classical ML algorithms as presented earlier, there are likewise DL algorithms such as CNN, FFNN, LSTM, GRU, and RNN, which are presently preferred for sentiment classification.…”

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confidence: 99%

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

et al. 2021

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At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and their extensive use by people, enormous amounts of data are produced hourly. However, sentiment analysis or opinion mining is considered as a useful tool that aims to extract the emotion and attitude from the user-posted data on social media platforms by using different computational methods to linguistic terms and various Natural Language Processing (NLP). Therefore, enhancing text sentiment classification accuracy has become feasible, and an interesting research area for many community researchers. In this study, a new Fuzzy Deep Learning Classifier (FDLC) is suggested for improving the performance of data-sentiment classification. Our proposed FDLC integrates Convolutional Neural Network (CNN) to build an effective automatic process for extracting the features from collected unstructured data and Feedforward Neural Network (FFNN) to compute both positive and negative sentimental scores. Then, we used the Mamdani Fuzzy System (MFS) as a fuzzy classifier to classify the outcomes of the two used deep (CNN+FFNN) learning models in three classes, which are: Neutral, Negative, and Positive. Also, to prevent the long execution time taking by our hybrid proposed FDLC, we have implemented our proposal under the Hadoop cluster. An experimental comparative study between our FDLC and some other suggestions from the literature is performed to demonstrate our offered classifier's effectiveness. The empirical result proved that our FDLC performs better than other classifiers in terms of true positive rate, true negative rate, false positive rate, false negative rate, error rate, precision, classification rate, kappa statistic, F1-score and time consumption, complexity, convergence, and stability.

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The Use of Mixed-Reality Sport Platforms in Social Media Sentiment Analysis during COVID-19

Dömök

Fodor

2022

Lecture Notes in Business Information Processing

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Sentiment Analysis on Online Product Reviews

Cited by 25 publications

References 13 publications

Sentiment Analysis Techniques and Application-Survey and Taxonomy

Sentiment Analysis Techniques and Application-Survey and Taxonomy

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

The Use of Mixed-Reality Sport Platforms in Social Media Sentiment Analysis during COVID-19

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