Purpose: Using analytics for firms' competitiveness is a vital component of a company's strategic planning and management process. In recent years, organizations have started to capitalize on the significant use of big data for analyses to gain valuable insights to improve decision-making processes. In this regard, leveraging and unleashing the potential of big data has become a significant success factor for steering firms' competitiveness, and the related literature is increasing at a very high pace. Thus, the authors propose a bibliometric study to understand the most important insights from these studies and enrich existing conceptual models.Design/methodology/approach: In this study, the authors use a bibliometric review on articles related to the use of big data for firms' competitiveness. The authors examine the contributions of research constituents (authors, institutions, countries and journals) and their structural and thematic relationships (collaborations, co-citations networks, co-word networks, thematic trends and thematic map). The most important insights are used to enrich a conceptual model.Findings: Based on the performance analysis results, the authors found that China is by far the most productive country in this research field. However, in terms of influence (by the number of citations per article), the most influential countries are the UK, Australia and the USA, respectively. Based on the science mapping analysis results, the most important findings are projected in the common phases of competitive intelligence processes and include planning and directions concepts, data collection concepts, data analysis concepts, dissemination concepts and feedback concepts. This projection is supplemented by cross-cutting themes such as digital transformation, cloud computing, privacy, data science and competition law. Three main future research directions are identified: the broadening of the scope of application fields, the specific case of managing or anticipating the consequences of pandemics or high disruptive events such as COVID-19 and the improvement of connection between firms' competitiveness and innovation practices in a big data context.Research limitations/implications: The findings of this study show that the most important research axis in the existing literature on big data and firms' competitiveness are mostly related to common phases of competitive intelligence processes. However, concepts in these phases are strongly related to the most important dimensions intrinsic to big data. The use of a single database (Scopus) or the selected keywords can lead to bias in this study. Therefore, to address these limitations, future studies could combine different databases (i.e. Web of Science and Scopus) or different sets of keywords.Practical implications: This study can provide to practitioners the most important concepts and future directions to deal with for using big data analytics to improve their competitiveness.Social implications: This study can help researchers or practitioners to identify potential research collaborators or identify suitable sources of publications in the context of big data for firms' competitiveness.Originality/value: The authors propose a conceptual model related to big data and firms' competitiveness from the outputs of a bibliometric study.

Sentiment analysis, a crucial component of natural language processing (NLP), involves the classification of subjective information by extracting emotional content from textual data. This technique plays a significant role in the movie industry by analyzing public opinions about films. The present research addresses a gap in the literature by conducting a comparative analysis of various machine learning algorithms for sentiment analysis in film reviews, utilizing a dataset from Kaggle comprising 50,000 reviews. Classifiers such as Logistic Regression, Multinomial Naive Bayes, Linear Support Vector Classification (LinearSVC), and Gradient Boosting were employed to categorize the reviews into positive and negative sentiments. The emphasis was placed on specifying and comparing these classifiers in the context of film review sentiment analysis, highlighting their respective advantages and disadvantages. The dataset underwent thorough preprocessing, including data cleaning and the application of stemming techniques to enhance processing efficiency. The performance of the classifiers was rigorously evaluated using metrics such as accuracy, precision, recall, and F1-score. Among the classifiers, LinearSVC demonstrated the highest accuracy at 90.98%. This comprehensive evaluation not only identified the most effective classifier but also elucidated the contextual efficiencies of various algorithms. The findings indicate that LinearSVC excels at accurately classifying sentiments in film reviews, thereby offering new insights into public opinions on films. Furthermore, the extended comparison provides a step-by-step guide for selecting the most suitable classifier based on dataset characteristics and context, contributing valuable knowledge to the existing literature on the impact of different machine learning approaches on sentiment analysis outcomes in the movie industry.

The task of interpreting multi-variable time series data, while also forecasting outcomes accurately, is an ongoing challenge within the machine learning domain. This study presents an advanced method of utilizing Long Short-Term Memory (LSTM) recurrent neural networks in the analysis of such data, with specific attention to both target and exogenous variables. The novel approach aims to extract hidden states that are unique to individual variables, thereby capturing the distinctive dynamics inherent in multi-variable time series and allowing the elucidation of each variable's contribution to predictive outcomes. A pioneering mixture attention mechanism is introduced, which, by leveraging the aforementioned variable-specific hidden states, characterizes the generative process of the target variable. The study further enhances this methodology by formulating associated training techniques that permit concurrent learning of network parameters, variable interactions, and temporal significance with respect to the target prediction. The effectiveness of this approach is empirically validated through rigorous experimentation on three real-world datasets, including the 2022 closing prices of three major stocks-Apple (AAPL), Amazon (AMZN), and Microsoft (MSFT). The results demonstrated superior predictive performance, attributable to the successful encapsulation of the diverse dynamics of different variables. Furthermore, the study provides a comprehensive evaluation of the interpretability outcomes, both qualitatively and quantitatively. The presented framework thus holds substantial promise as a comprehensive solution that not only enhances prediction accuracy but also aids in the extraction of valuable insights from complex multi-variable datasets.

This overview traced the evolution of data management, transitioning from traditional ETL processes to addressing contemporary challenges in Big Data, with a particular emphasis on data ingestion and schema matching. It explored the classification of data ingestion into batch, real-time, and hybrid processing, underscoring the challenges associated with data quality and heterogeneity. Central to the discussion was the role of schema mapping in data alignment, proving indispensable for linking diverse data sources. Recent advancements, notably the adoption of machine learning techniques, were significantly reshaping the landscape. The paper also addressed current challenges, including the integration of new technologies and the necessity for effective schema matching solutions, highlighting the continuously evolving nature of schema matching in the context of Big Data