Exploring the Power of Topic Modeling Techniques in Analyzing Customer Reviews: A Comparative Analysis

The exponential growth of online social network platforms and applications has led to a staggering volume of user-generated textual content, including comments and reviews. Consequently, users often face difficulties in extracting valuable insights or relevant information from such content. To address this challenge, machine learning and natural language processing algorithms have been deployed to analyze the vast amount of textual data available online. In recent years, topic modeling techniques have gained significant popularity in this domain. In this study, we comprehensively examine and compare five frequently used topic modeling methods specifically applied to customer reviews. The methods under investigation are latent semantic analysis (LSA), latent Dirichlet allocation (LDA), non-negative matrix factorization (NMF), pachinko allocation model (PAM), Top2Vec, and BERTopic. By practically demonstrating their benefits in detecting important topics, we aim to highlight their efficacy in real-world scenarios. To evaluate the performance of these topic modeling methods, we carefully select two textual datasets. The evaluation is based on standard statistical evaluation metrics such as topic coherence score. Our findings reveal that BERTopic consistently yield more meaningful extracted topics and achieve favorable results.Comment: 13 page

Optimizing Multi-Class Text Classification: A Diverse Stacking Ensemble Framework Utilizing Transformers

Customer reviews play a crucial role in assessing customer satisfaction, gathering feedback, and driving improvements for businesses. Analyzing these reviews provides valuable insights into customer sentiments, including compliments, comments, and suggestions. Text classification techniques enable businesses to categorize customer reviews into distinct categories, facilitating a better understanding of customer feedback. However, challenges such as overfitting and bias limit the effectiveness of a single classifier in ensuring optimal prediction. This study proposes a novel approach to address these challenges by introducing a stacking ensemble-based multi-text classification method that leverages transformer models. By combining multiple single transformers, including BERT, ELECTRA, and DistilBERT, as base-level classifiers, and a meta-level classifier based on RoBERTa, an optimal predictive model is generated. The proposed stacking ensemble-based multi-text classification method aims to enhance the accuracy and robustness of customer review analysis. Experimental evaluations conducted on a real-world customer review dataset demonstrate the effectiveness and superiority of the proposed approach over traditional single classifier models. The stacking ensemble-based multi-text classification method using transformers proves to be a promising solution for businesses seeking to extract valuable insights from customer reviews and make data-driven decisions to enhance customer satisfaction and drive continuous improvement

Unmasking Falsehoods in Reviews: An Exploration of NLP Techniques

In the contemporary digital landscape, online reviews have become an indispensable tool for promoting products and services across various businesses. Marketers, advertisers, and online businesses have found incentives to create deceptive positive reviews for their products and negative reviews for their competitors' offerings. As a result, the writing of deceptive reviews has become an unavoidable practice for businesses seeking to promote themselves or undermine their rivals. Detecting such deceptive reviews has become an intense and ongoing area of research. This research paper proposes a machine learning model to identify deceptive reviews, with a particular focus on restaurants. This study delves into the performance of numerous experiments conducted on a dataset of restaurant reviews known as the Deceptive Opinion Spam Corpus. To accomplish this, an n-gram model and max features are developed to effectively identify deceptive content, particularly focusing on fake reviews. A benchmark study is undertaken to explore the performance of two different feature extraction techniques, which are then coupled with five distinct machine learning classification algorithms. The experimental results reveal that the passive aggressive classifier stands out among the various algorithms, showcasing the highest accuracy not only in text classification but also in identifying fake reviews. Moreover, the research delves into data augmentation and implements various deep learning techniques to further enhance the process of detecting deceptive reviews. The findings shed light on the efficacy of the proposed machine learning approach and offer valuable insights into dealing with deceptive reviews in the realm of online businesses.Comment: 6 pages, 3 figure

Efficacy of cognitive pragmatic treatment on theory of mind functioning, quality of life and reduction of symptom severity in adults with schizophrenia

Objective: Schizophrenia is a severely debilitating disorder characterized by heterogeneous psychopathology, it impacts an individual’s subjective well-being, pragmatic communication skills, and cognitive functioning. The primary aim of this research was to evaluate the effectiveness of cognitive pragmatic treatment (CPT), an evidence-based group intervention program, on theory of mind (ToM) functioning, quality of life (QoL), and symptom severity of disorder in adults with schizophrenia. Methods: One hundred individuals diagnosed with schizophrenia were chosen and randomly split into two groups, as control group (n=25) and experimental group (n=75). Experimental group received CPT for 3 months, while the control group got only routine psychiatric care. The individuals were assessed for symptom severity of the disorder, ToM functioning and QoL before and after the intervention. 3-months post-intervention, a follow-up evaluation was carried out. The data were analysed using both parametric as well as nonparametric statistics. Results: The results of two-way Repeated Measure ANOVA found statistically significant differences between groups as well as tests (p<0.001) and between groups and their interaction with the tests (p<0.001). Experimental post-test as well as follow-up evaluation showed significant improvement in reducing the symptom severity of the disorder, improvement in ToM functioning and QoL compared to control group Conclusion: The current study demonstrates that cognitive pragmatic treatment as evidence-based intervention can improve theory of mind functioning, as well as QoL of individuals with schizophrenia, by reducing the symptom severity

Graph-based methods for cervical cancer segmentation: Advancements, limitations, and future directions

Cervical cancer remains a significant health concern worldwide, where precise segmentation of cervical lesions is integral for effective diagnosis and treatment planning. This systematic review critically evaluates the application of graph-based methodologies for cervical cancer segmentation, identifying their potential, drawbacks, and avenues for future development. An exhaustive literature search across Scopus and PubMed databases resulted in 20 pertinent studies. These studies were assessed focusing on their implementation of graph-based techniques for cervical cancer segmentation, the utilized datasets, evaluation metrics, and reported precision levels. The review highlights the progressive strides made in the field, especially regarding the segmentation of intricate, non-convex regions and facilitating the detection and grading of cervical cancer using graph-based methodologies. Nonetheless, several constraints were evident, including a dearth of comparative performance analysis, reliance on high-resolution images, difficulties in specific boundary delineation, and the imperative for additional validation and diversified datasets. The review suggests future work to integrate advanced deep learning strategies for heightened accuracy, formulate hybrid methodologies to counteract existing limitations, and explore multi-modal fusion to boost segmentation precision. Emphasizing the explainability and interpretability of outcomes also stands paramount. Lastly, addressing critical challenges such as scarcity of annotated data, the need for real-time and interactive segmentation, and the segmentation of multiple objects or regions of interest remains a crucial frontier for future endeavors

A novel type of quantum dot–transferrin conjugate using DNA hybridization mimics intracellular recycling of endogenous transferrin

International audienc

Fast, Efficient, and Stable Conjugation of Multiple DNA Strands on Colloidal Quantum Dots

A novel method for covalent conjugation of DNA to polymer coated quantum dots (QDs) is investigated in detail. This method is fast and efficient: up to 12 DNA strands can be covalently conjugated per QD in optimized reaction conditions. The QD-DNA conjugates can be purified using size exclusion chromatography and the QDs retain high quantum yield and excellent stability after DNA coupling. We explored single-stranded and double-stranded DNA coupling, as well as various lengths. We show that the DNA coupling is most efficient for short (15 mer) single-stranded DNA. The DNA coupling has been performed on QDs emitting at four different wavelengths, as well as on gold nanoparticles, suggesting that this technique can be generalized to a wide range of nanoparticles