Emotion classification is a critical component in understanding human expression and interaction in textual data. However, achieving high accuracy in emotion classification, particularly in complex languages like Hindi, presents several challenges. This article proposes a hybrid method for emotion classification in Hindi text, combining a knowledge-based approach for stability and a statistical approach for accuracy. Machine learning (ML) techniques, while powerful, require extensive training data and may not generalize well across different domains. Conversely, lexicon-based methods offer consistent performance across domains but often lack precision. To leverage the advantages of both methods, a hybrid model for emotion analysis in Hindi is presented. Results indicate that the hybrid model outperforms both individual classifiers and the simple lexicon approach in terms of accuracy. Specifically, incorporating Multinomial Naïve Bayes within the hybrid framework yields significant performance improvements. The proposed hybrid model presents a viable and superior approach for Hindi emotion analysis, balancing the need for stability and accuracy across varied textual domains

With an explosion of data on the web, consistent data access to diverse data sources has become a challenging task. In this tutorial will present topics of interest to database researchers and developers building: interoperable middle-ware, gateways, distributed heterogeneous query processors, federated databases, data source wrappers, mediators, and DBMS extensions.

The key driving force behind general-purpose enterprise directory services is for providing a central repository for commonly and widely used information such as users, groups, network service access information and profiles, security information, etc. Acceptance of the Lightweight Directory Access Protocol (LDAP) as an access protocol has facilitated widespread integration of these directory services into the network infrastructure and applications.

The downstream activities of large scale software systems life-cycle require team effort of several people over extended time periods. Current computing environments, exemplified by Unix, provide limited support for such team activities. Matisse is a knowledge-based team programming environment that addresses this issue. Matisse models source code, associated documents, people, and tasks as objects in an information system. Relationships between these objects provide associative information retrieval and navigational support; rules defined on object attributes facilitate team coordination and configuration management. We describe the design, implementation, and experimental use of Matisse. Matisse represents a novel information management architecture for team programming: It uses database technology for team information management and expert system technology for individual information management; some experimental data on accesses from the individual to the team information space is presented. Three hard scenarios: semiautomatic design history maintenance, dynamic object composition, and adaptive use of existing software, illustrate the power of Matisse, with respect to team programming, over traditional programming environments.