An Optimal Clustering with Hybrid Metaheuristic Algorithm for Sentiment Analysis and Classification

Modern review websites, namely Yelp and Amazon, permit the users to post online reviews for numerous businesses, services and products. Currently, online reviewing is an imperative task in the manipulation of shopping decisions produced by customers. These reviews afford consumers experience and information regarding the superiority of the product. The prevalent method of strengthening online review evolution is the performance of Sentiment Classification, which is an attractive domain in industrial and academic research. The review helps various domains, and it is problematic to collect interpreted training data. In this paper, an effectual Review Rating Prediction and Sentiment Classification was developed. Here, a Gated Recurrent Unit (GRU) was employed for the Sentiment Classification process, whereas a Hierarchical Attention Network (HAN) was applied for Review Rating Prediction. The significant features, such as statistical, SentiWordNet and classification features, were extracted for the Sentiment Classification and Review Rating Prediction process. Moreover, the GRU was trained by the designed TD-Spider Taylor ChOA approach, and the HAN was trained by the designed Jaya-TDO approach. The experimental results show that the proposed Jaya-TDO technique attained a better performance of 0.9425, 0.9654 and 0.9538, and that TD-Spider Taylor ChOA achieved 0.9524, 0.9698 and 0.9588 in terms of the precision, recall and F-measure.

Sentiment analysis is widely used today to make data-driven decisions in different industries, starting from marketing and including brand management, reputation monitoring, and customer satisfaction analysis. Its growing importance is closely linked with so-called ‘word-of-mouth’ communication, from reading online reviews to writing comments on social networks. Effective separation of sentiments ensures that companies' responses are timely and critical patterns are seen in big data sets. Statistical measures, information gain, correlation-based approaches, etc, have been employed for the feature selection. Still, the problem associated with text data mining is that they don’t convey the text's relative difficulty and additional features. To fill this gap, our research proposes a new feature selection technique through Ant Colony Optimization (ACO) and K Nearest Neighbour (KNN) performed on 28,000 customer reviews in different product categories. The results, therefore, showed an overall accuracy of 80.1%, with the Support Vector Machine (SVM) set at 80.5% on each selected feature, which was slightly higher than the Convolutional Neural Network (CNN), which scored a 78.41% accuracy. SVM remains on the mark of 83%, and for CNN, the rate achieved on the same was 80.8% when both were applied to the entire dataset. These facts rejected the infallibility of the simple and complex algorithms used singly in the sentiment classification, indicating that more sophisticated algorithms like ACO and KNN can provide business solutions to improve their service delivery based on customers’ feedback.

Sentiment analysis is the field of study that focuses on finding effectively the conduct of subjective text by analyzing people’s opinions, sentiments, evaluations, attitudes and emotions towards entities. The analysis of data and extracting the opinion word from the data is a challenging task especially when it involves reviews from completely different domains. We perform cross domain sentiment analysis on Amazon product reviews (books, dvd, kitchen appliances, electronics) and TripAdvisor hotel reviews, effectively classify the reviews to positive and negative polarities by applying various preprocessing techniques like Tokenization, POS Tagging, Lemmatization and Stemming which can enhance the performance of sentiment analysis in terms of accuracy and time to train the classifier. Various methods proposed for document-level sentiment classification like Naive Bayes, k-Nearest Neighbor, Support Vector Machines and Decision Tree are analysed in this work. Cross domain sentiment classification is useful because many times we might not have training corpus of specific domains for which we need to classify the data and also cross domain is favoured by lower computation cost and time. Despite poor performance in accuracy, the time consumed for sentiment classification when multiple testing datasets of different domains are present is far less in case of cross domain as compared to single domain. This work aims to define methods to overcome the problem of lower accuracy in cross-domain sentiment classification using different techniques and taking the benefit of being a faster method.

Opinion Mining and Sentiment Analysis acts as a pivotal role in facilitating businesses to actively operate on enhancing the business strategies and accomplish detailed insights of the consumer’s feedback regarding the products. In recent times, deep learning (DL)technique has been used for many sentiment analysis tasks and has attained effective outcomes. Huge quantity of product reviews is being posted by the customer on different e-commerce and social networking platforms which can assist the developers to improve the quality of the products. The study focuses on the design of Sentiment Classification on Online Product Reviews using Dwarf Mongoose Optimization with Attention based Deep Learning (DMO-ABDL) model. The proposed DMO-ABDL technique analyzes the product reviews for the identification of sentiments. To accomplish this, the DMO-ABDL technique performs different stages of preprocessing to transform the actual data into suitable format. Furthermore, the Glove technique is employed for word embedding process. Moreover, attention based long short-term memory (ALSTM) approach was exploited for sentiment classification and its hyperparameters can be optimally chosen by the DMO technique. A comprehensive set of experiments were performed in order to guarantee the enhanced sentiment classification performance of the DMO-ABDL algorithm. A brief comparative study highlighted the supremacy of the DMO-ABDL technique over other existing approaches under different measures.

Reviews of women's tops in the market are valuable information if processed properly. Merchants can conduct product review analysis to obtain information that can be used to evaluate products and services. Product review analysis activities are not enough just to see the number of stars, it is necessary to see the entire content of the review comments to be able to know the intent of the review. Sentiment analysis system is a system used to automatically analyze online product reviews to obtain information including sentiment information that is part of online reviews. The data is classified using Naive Bayes. The data collected were 1,000 product reviews of women's tops as samples. The purpose of this study is to determine the sentiment analysis of female top product reviews using the Naive Bayes algorithm. The stages of this research include data collection, labeling, pre-processing, sentiment classification, and evaluation. In the pre-processing stage there are 6 stages, namely Cleaning emoticons & symbols, Case folding, Word Normalizer, Tokenize, Stopword Removal and Stemming. TF-IDF (Term Frequency - Inverse Document Frequency) method is used for word weighting. The data will be classified into 3 (three) classes, namely negative, positive and neutral. The data will then be evaluated using accuracy parameter testing. The test results show an accuracy value of 89%, this result shows that the product reviews of women's tops are more positive.

Social media is emerging rapidly on the internet. This media knowledge helps people, company and organizations to analyze information for important decision making. Opinion mining is also called as sentiment analysis which involves in building a system to gather and examine opinions about the product made in reviews or tweets, comments, blog posts on the web. Sentiment is classified automatically for important applications such as opinion mining and summarization. To make valuable decisions in marketing analysis where implement sentiment classification efficiently. Reviews contain sentiment which is expressed in a different way in different domains and it is costly to annotate data for each new domain. The analysis of online customer reviews in which firms cannot discover what exactly people liked and did not like in document-level and sentence-level opinion mining. So, now opinion mining ongoing research is in phrase-level opinion mining. It performs finer-grained analysis and directly looks at the opinion in online reviews. The proposed system is based on phrase-level to examine customer reviews. Phrase-level opinion mining is also well-known as aspect based opinion mining. It is used to extract most important aspects of an item and to predict the orientation of each aspect from the item reviews. The projected system implements aspect extraction using frequent itemset mining in customer product reviews and mining opinions whether it is positive or negative opinion. It identifies sentiment orientation of each aspect by supervised learning algorithms in customer reviews.

In sentiment analysis, biased user reviews can have a detrimental impact on a company's evaluation. Therefore, identifying such users can be highly beneficial as their reviews are not based on reality but on their characteristics rooted in their psychology. Furthermore, biased users may be seen as instigators of other prejudiced information on social media. Thus, proposing a method to help detect polarized opinions in product reviews would offer significant advantages. This paper proposes a new method for sentiment classification of multimodal data, which is called UsbVisdaNet (User Behavior Visual Distillation and Attention Network). The method aims to identify biased user reviews by analyzing their psychological behaviors. It can identify both positive and negative users and improves sentiment classification results that may be skewed due to subjective biases in user opinions by leveraging user behavior information. Through ablation and comparison experiments, the effectiveness of UsbVisdaNet is demonstrated, achieving superior sentiment classification performance on the Yelp multimodal dataset. Our research pioneers the integration of user behavior features, text features, and image features at multiple hierarchical levels within this domain.

Sentiment analysis (SA) is becoming more and more irreplaceable when we talk about recommendation or analyze guest's preference. Sentiment classification gives prediction ratings or classifying the emotion into two or more poles. We are now looking forward to perfect the precision of the prediction of models. Nowadays, with the development of neural network, deep learning has been broadly used in sentiment analysis. In this paper, we survey large number of papers concerning sentiment classification with deep learning. We propose a new venue to make a division of all the models into 6 kinds. Moreover, we make a comparison of these results of models and draw a conclusion.

Sentiment classification aims to complete the automatic judgment task of text sentiment tendency. In the sentiment classification task of online reviews, traditional deep learning models require a large number of manually annotated samples of sentiment tendency for supervised training. Faced with massive online review data, the feasibility of manual tagging is worrisome. In addition, the traditional deep learning model ignores the imbalanced distribution of the number of classification samples, which will lead to a decline in classification performance in the practical application of the model. Considering that the online review data contains weak tagging information such as scores and labels, and the distribution is imbalanced, a weak tagging and imbalanced networks for online review sentiment classification is constructed. The experimental results show that the model significantly outperforms the traditional deep learning model in the sentiment classification task of hotel review data.

The Spiking Neural Network (SNN) model is a third-generation neural network model that uses spike pulse trains to detect and classify electrical problems in the electrical equipment under consideration. To diagnose the thermal issue in the early stages, it is required to evaluate and monitor the electrical components. The detection method in Infrared Thermography (IRT) is hybrid optimization, which is a Dragonfly Algorithm (DA)–Ant Colony Optimization (ACO) technique that produces a higher exploration and exploitation rate while moving toward an optimal solution with a higher convergence rate. The innovative hybrid DA–ACO method used in this study aims to find the best weights for the SNN model while also extracting the most useful characteristics for defect detection in electrical equipment. To improve classification accuracy, the optimum features picked using the created hybrid DA–ACO are provided to the SNN model. Based on conventional and other optimization methods, the proposed method provided superior results from the execution results.

<em>American Journal of Business and Operations Research (AJBOR)) Vol. 4, No. 2, PP. 49-56, 2021</em> <em>The paper is available here</em> <em>http://www.americaspg.com/articleslist/1/170/151</em>

Understanding customer sentiment through product reviews is important for businesses aiming to enhance their products and services. Traditional sentiment analysis often struggles to detect the wide range of emotional nuances, particularly those expressed through emojis, in online reviews. To address this challenge, our paper introduces a novel technique called Emoji-Enhanced BERT (EEBERT) based, on the BERT architecture. The Sentiment Adjustment Factor (SAF), created for emoji tokenization with the embeddings layer, is applied to analyze sentiment and emotion within review text content. Sentiments classification is evaluated using three different methods: <xref ref-type="disp-formula" rid="deqn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(1)</xref> evaluating the full review text; <xref ref-type="disp-formula" rid="deqn2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(2)</xref> analyzing the text with emojis; and <xref ref-type="disp-formula" rid="deqn3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(3)</xref> integrating star ratings and total votes in EEBERT. These methods were fine-tuned using a labeled Amazon product reviews dataset. The approach utilizes a well-vetted dataset comprising approximately 10 million customer reviews from four different categories. This dataset is condensed to 26,818 reviews to only consider emoji contained reviews, covering 3,275 distinct emojis. A systematic approach to classifying reviews and an innovative technique for assessing the emotional semantics of emojis are developed. The proposed model achieves an accuracy of 97.00%, further applies continuous testing via 5-fold cross-validation, achieving an average accuracy of 99.21%, which supports the reliability of the EEBERT model.

Sentiment classification is a key task in sentiment analysis, reviews mining, and other text mining applications. Various models have been proposed to build sentiment classifiers, but the classification performances of some existing methods are not good enough. Meanwhile, as a subproblem of sentiment classification, positive and unlabeled learning (PU learning) problem widely exists in real‐world cases, but it has not been given enough attention. In this article, we aim to solve the two problems in one framework. We first build a model for traditional sentiment classification based on adversarial learning, attention mechanism, and long short‐term memory (LSTM) network. We further propose an enhanced adversarial learning method to tackle PU learning problem. We conducted extensive experiments in three real‐world datasets. The experimental results demonstrate that our models outperform the compared methods in both traditional sentiment classification problem and PU learning problem. Furthermore, we study the effect of our models on word embedding. Finally, we report and discuss the sensitivity of our models to parameters.

Sentiment analysis of online reviews for electric vehicles using the SMAA-2 method and interval type-2 fuzzy sets

In this paper, fault diagnosis model based on the fusion of hybrid neural network (HNN) and ant colony optimization algorithm (ACOA) is presented. The associated rules are extracted based on rough set theory and are used as the theoretic basis of the connection mechanism of higher-order NN, which was composed with the feedforwardNN, the hybrid NN model is constructed. ACOA was used to optimize solving and to improve the generalization performance of HNN model. Hence the construction of presented model possesses theoretical significance and may ensure to take optimization performance and to enhance the generalization ability. Through the simulation and the analysis of fault-tolerance performance (FTP), it shows that the model based on the fusion of HNN and ACOA can effectively enhance the generalization ability and the FTP.