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Anatomy of Sentiment Analysis of Tweets Using Machine Learning Approach
Misbah Iram,Saif Ur Rehman,Shafaq Shahid,Sayeda Ambreen Mehmood International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.10
Sentiment analysis using social network platforms such as Twitter has achieved tremendous results. Twitter is an online social networking site that contains a rich amount of data. The platform is known as an information channel corresponding to different sites and categories. Tweets are most often publicly accessible with very few limitations and security options available. Twitter also has powerful tools to enhance the utility of Twitter and a powerful search system to make publicly accessible the recently posted tweets by keyword. As popular social media, Twitter has the potential for interconnectivity of information, reviews, updates, and all of which is important to engage the targeted population. In this work, numerous methods that perform a classification of tweet sentiment in Twitter is discussed. There has been a lot of work in the field of sentiment analysis of Twitter data. This study provides a comprehensive analysis of the most standard and widely applicable techniques for opinion mining that are based on machine learning and lexicon-based along with their metrics. The proposed work is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous, and polarized positive, negative or neutral. In order to validate the performance of the proposed framework, an extensive series of experiments has been performed on the real world twitter dataset that alter to show the effectiveness of the proposed framework. This research effort also highlighted the recent challenges in the field of sentiment analysis along with the future scope of the proposed work.
Optimization of twin gear-based pretreatment of rice straw for bioethanol production
Ahmed, Muhammad Ajaz,Rehman, Muhammd Saif Ur,Terá,n-Hilares, Ruly,Khalid, Saira,Han, Jong-In Elsevier 2017 Energy conversion and management Vol.141 No.-
<P><B>Abstract</B></P> <P>A laboratory twin-gear reactor (TGR) was investigated as a new means for the pretreatment of high solid lignocelluloses. Response surface methodology based on Box Behnken Design was used to optimize the enzymatic digestibility with respect to the pretreatment process variables: temperature of 50–90°C, NaOH concentration of 2–6% and no. of cycles of 30–60. The results revealed that the TGR-based pretreatment led to the significant structural alterations through increases in pore size, pore volume, cellulose crystallinity and surface area. SEM images also confirmed the surface modifications in the pretreated rice straw. A response surface quadratic model predicted 90% of the enzymatic digestibility, and it was confirmed experimentally and through the analysis of variance (ANOVA) as well. The TGR extrusion proved to be an effective means for exceedingly high solids lignocellulose.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Twin gear reactor is a continuous high solids pretreatment reactor. </LI> <LI> RSM was applied to optimize twin gear pretreatment for enzymatic digestibility. </LI> <LI> 89% enzymatic digestibility was achieved under optimum conditions. </LI> <LI> Thermomechanical pretreatment altered the structural features of rice straw. </LI> </UL> </P>
Mining public opinion: a sentiment based forecasting for democratic elections of Pakistan
Nawaz Asif,Ali Tariq,Hafeez Yaser,Rehman Saif ur,Rashid Muhammad Rizwan 대한공간정보학회 2022 Spatial Information Research Vol.30 No.1
Twitter has emerged as outstanding and most prominent social media in today’s technological age. The data proliferates in quick and words with its activities trigger get fast responses from the users. This platform is perfect for promoting political perspectives, particularly when election campaigns are on its peak. Political trends on Twitter media has been contemplated in the course of recent years. In the past research, both supervised and unsupervised methodologies have been used to analyze the Twitter trends. Most of the Tweet classification approaches utilized built in Dictionaries, Naı¨ve Bayes, K-Nearest Neighbors (KNN), decision tree and Support Vector Machines (SVM) classifiers. However, in case of democratic election these trends can be mined to predict the winning party. However, all such approaches produce poor results due to language issues, low accuracy, limited access to internet and lower literacy rate in less developed countries such as Pakistan. This research study, find the best possible way for collection of tweets related to different political parties and build a prediction model that may analyze sentiments and opinions expressed by peoples in their Tweets. In this research work, a prediction based model along with novel similarity measure has been proposed to predict the election results of political parties in Pakistan. The proposed work is composed of data collection, preprocessing, aspect extraction, aspect refinement and final prediction using Bayesian theorem. Form the experimental results, it is concluded that proposed approach perform better than existing techniques by obtaining almost 98% accuracy and efficiently cover the limitations of existing studies.
Mushtaq, Azeem,Cho, Hoon,Ahmed, Muhammad Ajaz,Rehman, Muhammad Saif Ur,Han, Jong-In Elsevier 2019 Journal of membrane science Vol.590 No.-
<P><B>Abstract</B></P> <P>In this study, a new method of fabricating highly electro-conductive membranes, namely electroplating, was developed and its product performance was examined via microalgae harvesting. In this method, a layer of silver nanowires (AgNWs) was first vacuum-deposited on a poly(ether sulfone) support, followed by electroplating of silver layer. The electroplated membrane (C-AgNWs) found to exhibit surpassingly enhanced electrical conductivity (3.9 × 10<SUP>4</SUP> S/cm) and a satisfactory level of mechanical stability under prolonged filtration. When microalgae, <I>Chlorella</I> sp. HS-2, was harvested via electro-filtration, the membrane exhibited the intended effect of fouling mitigation, both in continuous and intermittent electric fields. This was attributed to the enhanced electrostatic repulsive forces between foulants and membrane along with in-situ electro-bubble generation from the membrane, reducing the overall blockage of the membrane surface. The intermittent mode was able to effectively mitigate fouling and recover flux to its initial level, with the effect compromised in successive steps. The continuous mode, however, did not display such performance degradation over time, but an increase of 480% in permeate flux at 20 V/mm. All this supported that the electroplating can serve as a promising route for the sake of fabricating the workable electro-membranes, conductive membranes that have boundless application potentials.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Electroplating can be a useful method for workable electro-membrane fabrication. </LI> <LI> Highly electro-conductive (3.9 × 10<SUP>4</SUP> S/cm) yet stable electro-membrane can be synthesized by this method. </LI> <LI> The AgNWs-based electroplated membrane can be used for efficient microalgae harvesting. </LI> <LI> The fabricated membrane can exhibit good antifouling property in continuous and intermittent modes of electro-filtration. </LI> </UL> </P>
Mining public opinion: a sentiment based forecasting for democratic elections of Pakistan
Nawaz Asif,Ali Tariq,Hafeez Yaser,Rehman Saif ur,Rashid Muhammad Rizwan 대한공간정보학회 2022 Spatial Information Research Vol.30 No.2
Twitter has emerged as outstanding and most prominent social media in today’s technological age. The data proliferates in quick and words with its activities trigger get fast responses from the users. This platform is perfect for promoting political perspectives, particularly when election campaigns are on its peak. Political trends on Twitter media has been contemplated in the course of recent years. In the past research, both supervised and unsupervised methodologies have been used to analyze the Twitter trends. Most of the Tweet classification approaches utilized built in Dictionaries, Naı¨ve Bayes, K-Nearest Neighbors (KNN), decision tree and Support Vector Machines (SVM) classifiers. However, in case of democratic election these trends can be mined to predict the winning party. However, all such approaches produce poor results due to language issues, low accuracy, limited access to internet and lower literacy rate in less developed countries such as Pakistan. This research study, find the best possible way for collection of tweets related to different political parties and build a prediction model that may analyze sentiments and opinions expressed by peoples in their Tweets. In this research work, a prediction based model along with novel similarity measure has been proposed to predict the election results of political parties in Pakistan. The proposed work is composed of data collection, preprocessing, aspect extraction, aspect refinement and final prediction using Bayesian theorem. Form the experimental results, it is concluded that proposed approach perform better than existing techniques by obtaining almost 98% accuracy and efficiently cover the limitations of existing studies.