With the rapid advancement of information technology, online information has been exponentially growing day by day, especially in the form of text documents such as news events, company reports, reviews on products, stocks-related reports, medical reports, tweets, and so on. Due to this, online monitoring and text mining has become a prominent task. During the past decade, significant efforts have been made on mining text documents using machine and deep learning models such as supervised, semisupervised, and unsupervised. Our area of the discussion covers state-of-the-art learning models for text mining or solving various challenging NLP (natural language processing) problems using the classification of texts. This paper summarizes several machine learning and deep learning algorithms used in text classification with their advantages and shortcomings. This paper would also help the readers understand various subtasks, along with old and recent literature, required during the process of text classification. We believe that readers would be able to find scope for further improvements in the area of text classification or to propose new techniques of text classification applicable in any domain of their interest.

The introduction of pre-trained large language models (LLMs) has transformed NLP by fine-tuning task-specific datasets, enabling notable advancements in news classification, language translation, and sentiment analysis. This has revolutionized the field, driving remarkable breakthroughs and progress. However, the growing recognition of bias in textual data has emerged as a critical focus in the NLP community, revealing the inherent limitations of models trained on specific datasets. LLMs exploit these dataset biases and artifacts as expedient shortcuts for prediction. The reliance of LLMs on dataset bias and artifacts as shortcuts for prediction has hindered their generalizability and adversarial robustness. Addressing this issue is crucial to enhance the reliability and resilience of LLMs in various contexts. This survey provides a comprehensive overview of the rapidly growing body of research on shortcut learning in language models, classifying the research into four main areas: the factors of shortcut learning, the origin of bias, the detection methods of dataset biases, and understanding mitigation strategies to address data biases. The goal of this study is to offer a contextualized, in-depth look at the state of learning models, highlighting the major areas of attention and suggesting possible directions for further research.

This is an effort to analyze the reaction of stock prices of Indian public and private banks listed in NSE and BSE to the announcement of seven best case news events. Several recent studies have analyzed the correlation between stock prices and news announcements; however, there is no evidence on how private and public sector Indian bank stocks react to important news events independently. We examine these features by concentrating on a sample of banking and government news events. We classify these news events to create a group of negative and a group of positive tone of announcements (sentiments). The statistical results show that the negative banking news announcements had a one-month impact on private banks, with statistically significant negative mean CARs. However, with highly statistically substantial negative mean CARs, the influence of the negative banking news announcements on public banks was observed for two months after the news was published. Furthermore, the influence of the positive banking news on private banks persisted a month after the news was published. Positive banking news events had an influence on public banks for five days after they were published. The study concludes that public bank stocks react more to negative news announcements than positive news announcements in the same manner as the sentimental polarity of the news announcements as compared to private bank stocks. First, we retrieved the news articles published in prominent online financial news portals between 2017 and 2020, and the seven major news events were extracted and classified using multi-class text classification. The Random Forest classifier produced a significant accuracy of 94% with pre-trained embeddings of DistilBERT, a neural network model, which outperformed the traditional feature representation technique, TF-IDF. The training data for the classifier were balanced using the SMOTE sampling technique.

As digital commerce platforms grow increasingly intelligent and engaging, delivering a superior customer experience (CX) has become a strategic compulsion. Despite expanding interdisciplinary interest, clarity remains limited regarding how CX is conceptualized, measured, and transformed by emerging technologies. This systematic literature review synthesizes findings from 82 peer-reviewed studies published between 1980 and 2025 using the PRISMA 2020 framework to explore four key questions: how CX is defined in e-commerce, which dimensions influence it, what methods are used to evaluate it, and how technologies such as Artificial Intelligence (AI), agentic AI, and automation are used. The review reveals a shift from traditional, transactional, and usability-based interpretations towards dynamic, emotional, and co-created experiences. Trust, system-driven personalization, user autonomy, and customer engagement emerge as the four central influencing factors due to technological advances in AI and Agentic AI. Methodologically, CX research has evolved from survey-based models to structural equation modeling, sentiment analysis, and AI-integrated frameworks. Notably, intelligent systems are increasingly acting as co-creators of customer journeys, shaping real-time personalization and emotional resonance. This review offers a consolidated framework for understanding CX in the modern e-commerce context, provides actionable insights for platform designers, and proposes a future research agenda centered on factors and methods describing AI, agency, and emotional intelligence.

Machine learning has grown in popularity in recent years as a method for evaluating financial text data, with promising results in stock price projection from financial news. Various research has looked at the relationship between news events and stock prices, but there is little evidence on how different sentiments (negative, neutral, and positive) of such events impact the performance of stocks or indices in comparison to benchmark indices. The goal of this paper is to analyze how a specific banking news event (such as a fraud or a bank merger) and other co-related news events (such as government policies or national elections), as well as the framing of both the news event and news-event sentiment, impair the formation of the respective bank’s stock and the banking index, i.e., Bank Nifty, in Indian stock markets over time. The task is achieved through three phases. In the first phase, we extract the banking and other co-related news events from the pool of financial news. The news events are further categorized into negative, positive, and neutral sentiments in the second phase. This study covers the third phase of our research work, where we analyze the impact of news events concerning sentiments or linguistics in the price movement of the respective bank’s stock, identified or recognized from these news events, against benchmark index Bank Nifty and the banking index against benchmark index Nifty50 for the short to long term. For the short term, we analyzed the movement of banking stock or index to benchmark index in terms of CARs (cumulative abnormal returns) surrounding the publication day (termed as D) of the news event in the event windows of (−1,D), (D,1), (−1,1), (D,5), (−5,−1), and (−5,5). For the long term, we analyzed the movement of banking stock or index to benchmark index in the event windows of (D,30), (−30,−1), (−30,30), (D,60), (−60,−1), and (−60,60). We explore the deep learning model, bidirectional encoder representations from transformers, and statistical method CAPM for this research.

The proliferation of digital financial news offers unprecedented opportunities for automated analysis of market-moving events. This paper presents a framework for event-based sentiment analysis of financial news that leverages Large Language Models (LLMs). The approach brings together three complementary ideas: Retrieval-Augmented Generation (RAG) for contextual enhancement, Graph Neural Networks (GNNs) for modeling relationships between events, and a multi-agent ensemble for orchestrated reasoning. The methodology targets well-known difficulties in financial text processing, including domain-specific terminology, implicit event detection, and temporal reasoning, and it combines transformer-based event extraction with sentiment classification enhanced by external knowledge retrieval. We evaluate six model configurations on an aggregated corpus of 14,851 financial news samples. On the event-detection task, every configuration reaches a weighted F1-score of 100%; we show that this is a ceiling effect produced by a binary event/no-event formulation over a highly imbalanced dataset rather than evidence of a difficult problem being solved, and we discuss what it implies for how such systems should be evaluated. On three-way sentiment classification, the strongest configuration—the multi-agent ensemble—reaches 87.4% accuracy, narrowly ahead of a RoBERTa (Robustly Optimized BERT Pretraining Approach) baseline at 87.2%; however, because the gaps reported between models are small and we did not run significance testing, we report them as indicative rather than definitive. The GNN component is described as part of the proposed design, but it has not yet been validated experimentally, and we state this limitation explicitly. The framework produces interpretable, structured outputs suited to downstream use in algorithmic trading, risk assessment, and investment decision support, and the paper contributes a reusable financial NLP pipeline together with a candid account of where the current evidence is, and is not, convincing.