An Unsupervised Approach for Sentiment Analysis on Social Media Short Text Classification in Roman Urdu

During the last two decades, sentiment analysis, also known as opinion mining, has become one of the most explored research areas in Natural Language Processing (NIP) and data mining. Sentiment analysis focuses on the sentiments or opinions of consumers expressed over social media or different web sites. Due to exposure on the Internet, sentiment analysis has attracted vast numbers of researchers over the globe. A large amount of research has been conducted in English, Chinese, and other languages used worldwide. However, Roman Urdu has been neglected despite being the third most used language for communication in the world, covering millions of users around the globe. Although some techniques have been proposed for sentiment analysis in Roman Urdu, these techniques are limited to a specific domain or developed incorrectly due to the unavailability of language resources available for Roman Urdu. Therefore, in this article, we are proposing an unsupervised approach for sentiment analysis in Roman Urdu. First, the proposed model normalizes the text to overcome spelling variations of different words. After normalizing text, we have used Roman Urdu and English opinion lexicons to correctly identify users\u27 opinions from the text. We have also incorporated negation terms and stemming to assign polarities to each extracted opinion. Furthermore, our model assigns a score to each sentence on the basis of the polarities of extracted opinions and classifies each sentence as positive, negative, or neutral. In order to verify our approach, we have conducted experiments on two publicly available datasets for Roman Urdu and compared our approach with the existing model. Results have demonstrated that our approach outperforms existing models for sentiment analysis tasks in Roman Urdu. Furthermore, our approach does not suffer from domain dependency

Incremental composition process for the construction of component-based management

Cyber-physical systems (CPS) are composed of software and hardware components. Many such systems (e.g., IoT based systems) are created by composing existing systems together. Some of these systems are of critical nature, e.g., emergency or disaster management systems. In general, component-based development (CBD) is a useful approach for constructing systems by composing pre-built and tested components. However, for critical systems, a development method must provide ways to verify the partial system at different stages of the construction process. In this paper, for system architectures, we propose two styles: rigid architecture and flexible architecture. A system architecture composed of independent components by coordinating exogenous connectors is in flexible architecture style category. For CBD of critical systems, we select EX-MAN from flexible architecture style category. Moreover, we define incremental composition mechanism for this model to construct critical systems from a set of system requirements. Incremental composition is defined to offer preservation of system behaviour and correctness of partial architecture at each incremental step. To evaluate our proposed approach, a case study of weather monitoring system (part of a disaster management) system was built using our EX-MAN tool

Dynamic Butterfly Optimization Algorithm for Feature Selection

Feature selection represents an essential pre-processing step for a wide range of Machine Learning approaches. Datasets typically contain irrelevant features that may negatively affect the classifier performance. A feature selector can reduce the number of these features and maximise the classifier accuracy. This paper proposes a Dynamic Butterfly Optimization Algorithm (DBOA) as an improved variant to Butterfly Optimization Algorithm (BOA) for feature selection problems. BOA represents one of the most recently proposed optimization algorithms. BOA has demonstrated its ability to solve different types of problems with competitive results compared to other optimization algorithms. However, the original BOA algorithm has problems when optimising high-dimensional problems. Such issues include stagnation into local optima and lacking solutions diversity during the optimization process. To alleviate these weaknesses of the original BOA, two significant improvements are introduced in the original BOA: the development of a Local Search Algorithm Based on Mutation (LSAM) operator to avoid local optima problem and the use of LSAM to improve BOA solutions diversity. To demonstrate the efficiency and superiority of the proposed DBOA algorithm, 20 benchmark datasets from the UCI repository are employed. The classification accuracy, the fitness values, the number of selected features, the statistical results, and convergence curves are reported for DBOA and its competing algorithms. These results demonstrate that DBOA significantly outperforms the comparative algorithms on the majority of the used performance metrics