Nowadays, digital reviews and ratings of E-commerce platforms provide a better way for consumers to buy the products. E-commerce giants like Amazon, Flipkart, etc provide customers with a forum to share their experience and provide potential consumers with true evidence of the product's outcomes. To obtain useful insights from a broad collection of reviews, it is important to separate reviews into positive and negative feelings. In the proposed work, Sentiment Analysis is to be done on the consumer review data and categorize into positive and negative feelings. Naive Bayes Classification, LSTM and Support Vector Machine (SVM) were employed for the classification of reviews from the various classification models. Many of the current SA techniques for these customer online product review text data have low accuracy and often takes longer time in the course of training. In this research work, BERT Base Uncased model which is a powerful Deep Learning Model is presented to elucidate the issue of Sentiment Analysis. The BERT model gave an improved performance with good prediction and high accuracy compared to the other methods of Machine Learning in the experimental evaluation.

Improving the performance of aspect based sentiment analysis using fine-tuned Bert Base Uncased model

A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, K-nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate...), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.

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Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches.

User-generated content on numerous sites is indicative of users’ sentiment towards many issues, from daily food intake to using new products. Amid the active usage of social networks and micro-blogs, notably during the COVID-19 pandemic, we may glean insights into any product or service through users’ feedback and opinions. Thus, it is often difficult and time consuming to go through all the reviews and analyse them in order to recognize the notion of the overall goodness or badness of the reviews before making any decision. To overcome this challenge, sentiment analysis has been used as an effective rapid way to automatically gauge consumers’ opinions. Large reviews will possibly encompass both positive and negative opinions on different features of a product/service in the same review. Therefore, this paper proposes an aspect-oriented sentiment classification using a combination of the prior knowledge topic model algorithm (SA-LDA), automatic labelling (SentiWordNet) and ensemble method (Stacking). The framework is evaluated using the dataset from different domains. The results have shown that the proposed SA-LDA outperformed the standard LDA. In addition, the suggested ensemble learning classifier has increased the accuracy of the classifier by more than ~3% when it is compared to baseline classification algorithms. The study concluded that the proposed approach is equally adaptable across multi-domain applications.

https://www.mdpi.com/2076-3417/12/8/4066/pdf?version=1650262981

Multi-Aspect Oriented Sentiment Classification: Prior Knowledge Topic Modelling and Ensemble Learning Classifier Approach

Sentiment analysis is an effective text categorization technique that is often used. It analyses any given text or comment and categorizes it as positive or negative depending on the viewpoints expressed. Previous sentiment categorization research has relied on either a traditional-based approach or machine learning approaches. Similarly, a fundamental flaw in earlier systems was they focused solely on only two-way classification or tri-classification of reviews, i.e., classifying the review as positive, negative, or neutral. Denial of the highly positive and highly negative reviews will lead to a misconception of the user's opinion of a product or movie, causing a trend or business to suffer. When solely a lexicon-based method is used, the system is heavily reliant on the lexicon resource and dictionary chosen. The performance of a machine learning system is determined by the algorithms used. Beyond the binary or tri classification of the customer's review, the suggested work integrates a traditional based approach (SentiWordNet) with statistical methods such as Decision Tree (DT), Naive Bayes (NB), Logistic Regression (LR), and SVM are used to solve the aforesaid challenges. These machine learning methods were compared to a lexicon-based strategy in terms of performance. The results showed that the Logistic Regression and SVM methods outperformed the rest two algorithms.

Fine - Grained Sentiment Analysis on Online Reviews

Novel heuristic bidirectional-recurrent neural network framework for multiclass sentiment analysis classification using coot optimization

Sentiment Analysis is a widely used text classification technique. It breaks down any given text or comments and classify the text either as positive or negative based on the views conveyed in it. Previous works done on sentiment classification used either lexicon based approach or machine learning techniques. Likewise, the major drawback of the existing systems was the focus on only binary classification of review such as positive or negative. Ignorance of the neutral review will result in misinterpretation of a customer’s opinion about a product or movie, which will degrade the business or trend. In case of using only lexicon based approach, the system highly depends on the selection of lexicon resource and dictionary. In case of system built only using machine learning approach, the performance of the system depends on the algorithms chosen. This work presents a hybrid model to resolve the neutral class too. The proposed work combines a lexical approach (SentiWordNet) with the machine learning algorithms such as Support Vector Machine, Decision Tree, Logistic Regression and Naive Bayes for sentiment analysis to resolve the neutral opinions beyond the binary categorization of the customer’s review. We have also compared the performance of these four machine learning algorithms along with the lexicon approach. The results proved that Support Vector Machine and Logistic Regression algorithms outperform the other two algorithms with an accuracy of about 80% which is on average differs by 6% to 10% when compared to other algorithms.

M. Mahalakshmi

Papers

Sentiment Analysis for Predicting Customer Reviews using a Hybrid Approach