Sentiment Analysis Of Customer Reviews Research Articles

Aspect-Level Sentiment Analysis of Customer Reviews Based on Neural Multi-task Learning

In the era of big data, major e-commerce platforms are facing the challenge of an exponential growth in the number of user comments. Effectively utilizing these comments has become an urgent issue. Traditional manual statistical methods are no longer able to meet the demands for accuracy and real-time analysis. The rise of artificial intelligence-based text mining techniques provides a new approach to address this problem. By building deep learning analysis models, it is possible to uncover user preferences and product characteristics, helping businesses proactively adjust sales strategies, improve product and service quality, and achieve precise marketing. Text sentiment analysis is one of the important research directions in the field of text mining. Its basic idea is to transform subjective texts with emotional color into structured data, and then use machine learning, deep learning, and other artificial intelligence technologies to extract emotional features and discover knowledge patterns. This article proposes a deep learning-based sentiment analysis model, which includes a shared sentiment prediction layer used to transfer emotional knowledge between different aspect categories and alleviate the problem of insufficient data. The model consists of two parts: the Aspect Category Detection (ACD) classifier based on attention mechanism and the Aspect Category Sentiment Analysis (ACSA) classifier. The ACD part generates word weights using attention mechanism, while the ACSA part predicts the sentiment of words and combines weights to determine the sentiment of aspect categories within sentences.

Sentiment analysis of customer reviews for Algerian dialect using DziriBERT model

Sentiment analysis of customer reviews for Algerian dialect using DziriBERT model

Review Paper on Social Network for Problem Solving and Knowledge Sharing

This review paper presents a comprehensive analysis of 10 selected research papers that investigate the interplay between social media, gamification, and sentiment analysis. The papers cover a wide range of topics, including customer engagement, user behaviour in mobile apps, sustainable behaviour change, opinion mining, sentiment analysis techniques, and applications of sentiment analysis on social media data. The review offers valuable insights into the effects of social media and gamification on customer engagement by examining empirical studies and conceptual models In the realm of sentiment analysis, the reviewed papers shed light on opinion mining and sentiment analysis techniques applied to social media data. They explore the use of machine learning and lexicon-based approaches for sentiment analysis, along with the application of convolutional neural networks with word embedding for sentiment analysis of customer reviews. Furthermore, the review presents a comprehensive survey of techniques and applications of sentiment analysis on social media data. This comprehensive review contributes to the existing knowledge by offering a holistic perspective on the relationships between social media, gamification, and sentiment analysis. It underscores the significance of these areas in understanding customer engagement, user behaviour, and sentiment analysis on social media platforms. The findings from these papers have implications for researchers, practitioners, and professionals working in the fields of social media, gamification, and sentiment analysis, providing valuable insights for further exploration and practical implementation.

Sentiment Analysis of Customer Reviews Using Support Vector Machine and Smote-Tomek Links For Identify Customer Satisfaction

Shopping activities in the online market, especially fashion trends, continue to increase with all the promo efforts offered. One of the considerations for buying products on the online market is to read reviews. Each consumer review shows the level of interest in the product. The number of negative reviews and the emergence of many varied reviews pose a problem in categorizing reviews. Sentiment analysis is a way of looking at the polarity of reviews to classify positive and negative reviews. The Support Vector Machine method and the combination of the Synthetic Minority Oversampling Technique (SMOTE) with Tomek Links are applied in this study. Classification using the Support Vector Machine method and the combination of the Synthetic Minority Oversampling Technique (SMOTE) with Tomek Links showed better results with an Accuracy of 0.92, Precision of 0.89, Recall of 0.89, and F1-score of 0.89 than without the combination of the Synthetic Minority oversampling Technique (SMOTE) with Tomek Links with an Accuracy of 0.68, Precision of 0.55, Recall of 0.99, and an F1-score of 0.71.

Sentimental Analysis of Customer Reviews using RPA and Categorization of Reviews using ChatGPT

Reviews and feedback are very important for traveling websites and service providers. Reviews have been shown to be important in the decision-making process. Organizations and businesses are able to improve their goods and services thanks to comments and reviews. It is impossible to read every single review due to the vast number of client input on e-commerce sites including forums, review sites, and blogs. The current approaches for sentiment analysis paint a broad picture of how customers feel. But in order to examine each customer's emotions separately and resolve them, we need to automate this operation. Realistically speaking, reading every review and performing an analysis would be impossible or highly time- and resource-intensive. The model for sentiment analysis of customer reviews using robotic process automation and categorization of the reviews using openAI is proposed in this study. After analysing each customer's sentiment towards a certain airline, ChatGPT was used to categorise the reviews. As a result, we now receive feedback in categories and are aware of each client's emotion, making it simple to enhance the customer experience.

Machine Learning Applications for Sentiment Analysis of Customer Reviews in Natural Language

Machine Learning Applications for Sentiment Analysis of Customer Reviews in Natural Language

Unscrambling Customer Recommendations: A Novel LSTM Ensemble Approach in Airline Recommendation Prediction Using Online Reviews

Customer feedback is an essential criterion for upcoming customers to learn from their experience with a company’s products. Customer reviews and ratings also help companies improve performance and figure out new methodologies to provide better services. This research concentrates on customer reviews and ratings to investigate which product a customer evaluates and its association with its recommendations. This work predicts the user recommendations in two modules. The first module performs sentiment analysis of customer reviews using the long short-term memory (LSTM) model, which estimates the probability of the customer’s sentiment about the airline’s services. The second module experimented over only various service aspect ratings on different airline services provided by customers. These two modules ensemble together to determine the predictive recommendations of the airlines. The obtained results reinforce the essential theoretical contribution to the literature on service appraisal, online review, and recommendations. In addition, our proposed ensemble approach will be helpful to those practitioners who wish to use any proposal that will provide a quick and essential vision by bringing together customer-generated reviews and ratings, thereby helping them in strategy designing, service improvement, and post-purchases planning. Also, forthcoming travelers may benefit from this proposed approach by assimilating an aggregating view of service quality.

A novel packaging evaluation method using sentiment analysis of customer reviews

Packaging has an important role in protecting products during the distribution process. Therefore, it is essential for the packaging designer to evaluate the packaging even after the design implementation. While every new packaging design needs to go through a series of physical tests to ensure it delivers the product to the customer safely, it is not uncommon to encounter packaging failures after the design implementation. Redesigning a completely new package and redoing these tests is very expensive and a time‐consuming process. Therefore, an alternative evaluation method is needed to enhance efficiency while maintaining evaluation quality. The main purpose of this paper is the evaluation of package performance during distribution using customer reviews. Sentiment analysis (SA) is implemented to identify the positive or negative sentiment from customer reviews. Moreover, the proposed method utilizes an in‐house library containing packaging related words (Pack‐List) to identify the reviews concerning the packaging. As a result, this method provides a systematic approach to detect problems by analysing customer reviews as feedback to a defective packaging product. By using the results of SA with Pack‐List, a percentage of negative and positive reviews are calculated to examine the packaging performance during distribution. Then, the percentage of failure over various months and years is examined to identify how time affects the packaging failure. Next, a word cloud of negative sentences is created to show the most mentioned issue. With the proposed method, packaging designers can identify packaging problems in the early stages and keep track of the packaging performance.

Sentiment Analysis of Customer Reviews of Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence: Systematic Review.

During the COVID-19 crisis, customers’ preference in having food delivered to their doorstep instead of waiting in a restaurant has propelled the growth of food delivery services (FDSs). With all restaurants going online and bringing FDSs onboard, such as UberEATS, Menulog or Deliveroo, customer reviews on online platforms have become an important source of information about the company’s performance. FDS organisations aim to gather complaints from customer feedback and effectively use the data to determine the areas for improvement to enhance customer satisfaction. This work aimed to review machine learning (ML) and deep learning (DL) models and explainable artificial intelligence (XAI) methods to predict customer sentiments in the FDS domain. A literature review revealed the wide usage of lexicon-based and ML techniques for predicting sentiments through customer reviews in FDS. However, limited studies applying DL techniques were found due to the lack of the model interpretability and explainability of the decisions made. The key findings of this systematic review are as follows: 77% of the models are non-interpretable in nature, and organisations can argue for the explainability and trust in the system. DL models in other domains perform well in terms of accuracy but lack explainability, which can be achieved with XAI implementation. Future research should focus on implementing DL models for sentiment analysis in the FDS domain and incorporating XAI techniques to bring out the explainability of the models.

SENTIMENT ANALYSIS OF CUSTOMER REVIEWS

Sentiment analysis is a process in which we classify text data as positive, negative, or neutral or into some other category, which helps understand the sentiment behind the data. Mainly machine learning and natural language processing methods are combined in this process. One can find customer sentiment in reviews, tweets, comments, etc. A company needs to evaluate the sentiment behind the reviews of its product. Customer sentiment can be a valuable asset to the company. This ultimately helps the company make better decisions regarding its product marketing and improving product quality. This paper focuses on the sentiment analysis of customer reviews from Amazon. The reviews contain textual feedback along with a rating system. The aim is to build a supervised machine learning model to classify the review as positive or negative. As reviews are in the text format, there is a need to vectorize the text to numerical format for the computer to process the data. To do this, we use the Bag-of-words model and the TF-IDF (Term Frequency-Inverse Document Frequency) model. These two models are related to each other, and the aim is to find which model performs better in our case. The problem in our case is a binary classification problem; the logistic regression algorithm is used. Finally, the performance of the model is calculated using a metric called the F1 score.

Transformer based Contextual Model for Sentiment Analysis of Customer Reviews: A Fine-tuned BERT

The Bidirectional Encoder Representations from Transformers (BERT) is a state-of-the-art language model used for multiple natural language processing tasks and sequential modeling applications. The accuracy of predictions from context-based sentiment and analysis of customer review data from various social media platforms are challenging and time-consuming tasks due to the high volumes of unstructured data. In recent years, more research has been conducted based on the recurrent neural network algorithm, Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) as well as hybrid, neutral, and traditional text classification algorithms. This paper presents our experimental research work to overcome these known challenges of the sentiment analysis models, such as its performance, accuracy, and context-based predictions. We’ve proposed a fine-tuned BERT model to predict customer sentiments through the utilization of customer reviews from Twitter, IMDB Movie Reviews, Yelp, Amazon. In addition, we compared the results of the proposed model with our custom Linear Support Vector Machine (LSVM), fastText, BiLSTM and hybrid fastText-BiLSTM models, as well as presented a comparative analysis dashboard report. This experiment result shows that the proposed model performs better than other models with respect to various performance measures.

Sentiment Analysis Using Gated Recurrent Neural Networks

Text sentiment analysis is an important and challenging task. Sentiment analysis of customer reviews is a common problem faced by companies. It is a machine learning problem made demanding due to the varying nature of sentences, different lengths of the paragraphs of text, contextual understanding, sentiment ambiguity and the use of sarcasm and comparatives. Traditional approaches to sentiment analysis use the tally or recurrence of words in a text which are allotted sentiment values by some expert. These strategies overlook the order of words and the complex different meanings they can communicate. Hence, RNNs were introduced that are effective yet challenging to train. Bi-GRUs and Bi-LSTM architectures are a recent form of RNNs which can store information about long-term dependencies in sequential data. In this work, we attempted a survey of different deep learning techniques that have been applied to sentiment classification and analysis. We have implemented the baseline models for LSTM, GRU and Bi-LSTM and Bi-GRU on an Amazon review dataset.

Identifying and Profiling Key Sellers in Cyber Carding Community: AZSecure Text Mining System

The past few years have witnessed millions of credit/debit cards flowing through the underground economy and ultimately causing significant financial loss. Examining key underground economy sellers has both practical and academic significance for cybercrime forensics and criminology research. Drawing on social media analytics, we have developed the AZSecure text mining system for identifying and profiling key sellers. The system identifies sellers using sentiment analysis of customer reviews and profiles sellers using topic modeling of advertisements. We evaluated the AZSecure system on eight international underground economy forums. The system significantly outperformed all benchmark machine-learning methods on identifying advertisement threads, classifying customer review sentiments, and profiling seller characteristics, with an average F-measure of about 80 percent to 90 percent. In our case study, we identified the famous carder, Rescator, who was affiliated with the Target breach, and captured important seller characteristics in terms of product type, payment options, and contact channels. Our research leverages social media analytics to probe into the underground economy in order to help law enforcement target key sellers and prevent future fraud. It also contributes to our understanding of the use of information technology in detecting deception in online systems.

Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews

With the rapid growth of user-generated content on the internet, automatic sentiment analysis of online customer reviews has become a hot research topic recently, but due to variety and wide range of products and services being reviewed on the internet, the supervised and domain-specific models are often not practical. As the number of reviews expands, it is essential to develop an efficient sentiment analysis model that is capable of extracting product aspects and determining the sentiments for these aspects. In this paper, we propose a novel unsupervised and domain-independent model for detecting explicit and implicit aspects in reviews for sentiment analysis. In the model, first a generalized method is proposed to learn multi-word aspects and then a set of heuristic rules is employed to take into account the influence of an opinion word on detecting the aspect. Second a new metric based on mutual information and aspect frequency is proposed to score aspects with a new bootstrapping iterative algorithm. The presented bootstrapping algorithm works with an unsupervised seed set. Third, two pruning methods based on the relations between aspects in reviews are presented to remove incorrect aspects. Finally the model employs an approach which uses explicit aspects and opinion words to identify implicit aspects. Utilizing extracted polarity lexicon, the approach maps each opinion word in the lexicon to the set of pre-extracted explicit aspects with a co-occurrence metric. The proposed model was evaluated on a collection of English product review datasets. The model does not require any labeled training data and it can be easily applied to other languages or other domains such as movie reviews. Experimental results show considerable improvements of our model over conventional techniques including unsupervised and supervised approaches.