Figurative Language Detection using Deep Learning and Contextual Features

The size of data shared over the Internet today is gigantic. A big bulk of it comes from postings on social networking sites such as Twitter and Facebook. Some of it also comes from online news sites such as CNN and The Onion. This type of data is very good for data analysis since they are very personalized and specific. For years, researchers in academia and various industries have been analyzing this type of data. The purpose includes product marketing, event monitoring, and trend analysis. The highest usage for this type of analysis is to find out the sentiments of the public about a certain topic or product. This field is called sentiment analysis. The writers of such posts have no obligation to stick to only literal language. They also have the freedom to use figurative language in their publications. Hence, online posts can be categorized into two: Literal and Figurative. Literal posts contain words or sentences that are direct or straight to the point. On the contrary, figurative posts contain words, phrases, or sentences that carry different meanings than usual. This could flip the whole polarity of a given post. Due to this nature, it can jeopardize sentiment analysis works that focus primarily on the polarity of the posts. This makes figurative language one of the biggest problems in sentiment analysis. Hence, detecting it would be crucial and significant. However, the study of figurative language detection is non-trivial. There have been many existing works that tried to execute the task of detecting figurative language correctly, with different methodologies used. The results are impressive but still can be improved. This thesis offers a new way to solve this problem. There are essentially seven commonly used figurative language categories: sarcasm, metaphor, satire, irony, simile, humor, and hyperbole. This thesis focuses on three categories. The thesis aims to understand the contextual meaning behind the three figurative language categories, using a combination of deep learning architecture with manually extracted features and explore the use of well know machine learning classifiers for the detection tasks. In the process, it also aims to describe a descending list of features according to the importance. The deep learning architecture used in this work is Convolutional Neural Network, which is combined with manually extracted features that are carefully chosen based on the literature and understanding of each figurative language. The findings of this work clearly showed improvement in the evaluation metrics when compared to existing works in the same domain. This happens in all of the figurative language categories, proving the framework’s possession of quality

The Impact of key Macroeconomic factors on Economic Growth of Bangladesh: a VAR Co-integration Analysis

This study analyzes the impact of key macroeconomic factors on economic growth of Bangladesh from the period of 1988 to 2012.The key macroeconomic factors studied are market capitalization, foreign direct investment and real interest rate. This study also examines the long run and short run relationship between the economic growth and capital market, foreign direct investment, and real interest rate by using vector autoregressive (VAR) model. The VAR results suggest that the market capitalization, foreign direct investment and real interest rate have impact on economic growth in the long run, but in short run it does not have any predictable behavior. The variance decomposition results also conclude the same result as VAR model. All variables have the long run effects on economic growth but it does not have in short run, and the effects increases with time. Based on the finding, this study suggests that the government should come out with the appropriate macroeconomic plan and policy to draw more inward foreign direct investment, increase market capitalization and stabilize real interest rate in order to faster the economic growth in future. As finding of this study shows that these factors do not have significant impact on economic growth in Bangladesh in the short ru

HINDRANCE TO EFFECTIVE ENGLISH SPEAKING AT HIGHER SECONDARY LEVEL: A CASE STUDY ON THE COLLEGES OF DHAKA CITY IN BANGLADESH

The paper aims at investigating the prevailing barriers that the learners of higher secondary level in Dhaka city encounter in speaking English effectively in their classrooms. It reconnoitres the challenges that prevail in the teaching-learning environment and impede rehearsing English speaking inside the classroom. English has been inescapably used in everyday life in this period of intense global competition. But for various reasons, students in higher secondary level often struggle to develop adequate speaking skills.And these obstacles affect in their upcoming higher study, going abroad as well as in their future career. This study aims to locate the hindrances that most of the students of the level face in speaking English fluently in the classroom. A systematic study is conducted using a quantitative approach, and a questionnaire is used to obtain data.The findings accentuate the unreachability of logistic, managerial, and administrative amenities; unapproachability of pertinent teaching-learning techniques, methods and approaches, inaccessibility of reciprocated and accommodating setting that reinforce the core impediments in teaching-learning English speaking at higher secondary stage. This paper pinpoints all those problems and presents probable recommendations to develop English speaking competency. Future scholars will benefit from this study’s insights as they investigate the difficulties faced by English language learners in Bangladesh.Keywords: Competency, Dhaka, effective English speaking, higher secondary level, hindrance

Factors that influence consumer purchasing behaviour towards green product: a case on cocoa / Mohamad Saifullah Md Sam

Cocoa is one of major crop in Malaysia after oil palm, rubber and paddy. In 2014, the total cultivated by region and sector is 16,070 ha. The land cultivated by this species has been reduced in large amount of hectare. Cocoa industry of import and export show the good development in cocoa processing from raw material into product form. However, development and marketing of cocoa green product still lack and need improvement. Therefore, survey study has been developed to find the factors that influence consumer to purchase green product. Merlimau is the place for survey purpose. Green product is product that has been produced without affecting the environmental surrounding. The study wants to investigate the consumer purchasing behaviour by explaining the relation of four factors; environmental concern, health, social influence and price. The primary data need to be collected from respondents by using self-administered survey. All data from respondents will be analyzed by descriptive analysis, reliability analysis, correlation analysis and regression analysis to achieve the objectives in this study. The result shows that price is the most factor influence consumer purchasing behaviour. It is hope that government and other agencies take the action in improvement of green product especially on cocoa

Improvement of voltage stability and loadability of power system employing the placement of unified power flow controller using artificial neural network

This paper proposes a voltage stability and loadability improvement model of power systems by incorporating the optimal placement of flexible alternating current transmission systems (FACTS) using an artificial neural network (ANN) called OPFANN. The key aspect of this model is to identify the weakest lines which having the most probability of voltage collapse utilized for placing FACTS devices. As installing a new power system network with rapidly increasing power demand cannot be possible, the operator usually operates the power system close to the stability limit. In this regard, continuous monitoring and improvement of system voltage stability and loadability of the existing system are vital issues for energy management systems nowadays. However, the proposed OPFANN introduces a more straightforward and faster scheme for voltage stability monitoring systems using ANN. Intelligent and reliable data samples have been designed to train the ANN based on two-line voltage stability indices (LVSI) techniques. Compared with other works, OPFANN effectively improves voltage stability and loadability at the load point by installing the unified power flow controller (UPFC) FACTS devices to the weakest lines. OPFANN can provide information on voltage collapse points using ANN and reduce the further computational cost of LVSI. Finally, OPFANN ensures faster and more secure operation of the power system

Preliminary phytochemical screening and antidiarrhoeal activity of derris trifoliata lour

The present study was designed to investigate the antidiarrhoeal potential of 80% ethanol extract of aerial parts of Derris trrifoliata (DT) on castor oil-induced diarrhea in mice. Phytochemical screening of the plant extracts for their active constituents was also carried out using standard procedures. Oral administration of ethanol extract of DT (500 and 1000 mg/kg) significantly, and dose-dependently delayed the onset of diarrhoea induced by castor oil and also significantly reduced the number of diarrhoeal episodes and the number of animals exhibiting diarrhoea. The results were comparable to those of standard antimotility drug, hyoscine butylbromide (50 mg/kg). Phytochemical screening revealed the presence of steroid, flavonoid, reducing sugar, tannin, gum and saponin as major constituents. The results point out the presence of some active principles in DT extract possessing anti-diarrhoeal effect and substantiate the use of this herbal remedy as a non-specific treatment for diarrhoea in folk medicine

ADAPTIVE SECURE AND EFFICIENT ROUTING PROTOCOL FOR ENHANCE THE PERFORMANCE OF MOBILE AD HOC NETWORK

Nowadays Mobile Ad Hoc Network (MANET) is an emerging area of research to provide various communication services to end users. Mobile Ad Hoc Networks (MANETs) are self-organizing wireless networks where nodes communicate with each other without a fixed infrastructure. Due to their unique characteristics, such as mobility, autonomy, and ad hoc connectivity, MANETs have become increasingly popular in various applications, including military, emergency response, and disaster management. However, the lack of infrastructure and dynamic topology of MANETs pose significant challenges to designing a secure and efficient routing protocol. This paper proposes an adaptive, secure, and efficient routing protocol that can enhance the performance of MANET. The proposed protocol incorporates various security mechanisms, including authentication, encryption, key management, and intrusion detection, to ensure secure routing. Additionally, the protocol considers energy consumption, network load, packet delivery fraction, route acquisition latency, packets dropped and Quality of Service (QoS) requirements of the applications to optimize network performance. Overall, the secure routing protocol for MANET should provide a reliable and secure communication environment that can adapt to the dynamic nature of the network. The protocol should ensure that messages are delivered securely and efficiently to the intended destination, while minimizing the risk of attacks and preserving the network resources Simulation results demonstrate that the proposed protocol outperforms existing routing protocols in terms of network performance and security. The proposed protocol can facilitate the deployment of various applications in MANET while maintaining security and efficiency

ReviewRanker: A Semi-Supervised Learning Based Approach for Code Review Quality Estimation

Code review is considered a key process in the software industry for minimizing bugs and improving code quality. Inspection of review process effectiveness and continuous improvement can boost development productivity. Such inspection is a time-consuming and human-bias-prone task. We propose a semi-supervised learning based system ReviewRanker which is aimed at assigning each code review a confidence score which is expected to resonate with the quality of the review. Our proposed method is trained based on simple and and well defined labels provided by developers. The labeling task requires little to no effort from the developers and has an indirect relation to the end goal (assignment of review confidence score). ReviewRanker is expected to improve industry-wide code review quality inspection through reducing human bias and effort required for such task. The system has the potential of minimizing the back-and-forth cycle existing in the development and review process. Usable code and dataset for this research can be found at: https://github.com/saifarnab/code_revie