Pub Date : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315826
Umra Khan, S. Masood
Human Activity Recognition (HAR) is the problem of classifying an individual's activity into well-defined moments, utilizing responsive sensors that are influenced by human movement. Sensor-enabled smartphones make Human Activity Recognition progressively significant and well known. The physical sensors, gyroscope and accelerometer combinedly allow the devices to provide motion measuring capabilities in a more accurate manner. The present research work adopts a machine learning based approach for recognizing activity on the basis of data collected through the smartphone sensors (accelerometer and gyroscope). Various state-of-the-art machine learning based techniques have been employed and compared on the basis of the performance metrics, accuracy, recall, precision, and the F1-score. Of all the selected different machine learning classifiers, the best result is given by the Support Vector Machine (SVM) with ‘RBF’ kernel, which achieved an accuracy of 96.61 % in classifying the activities into the six different classes.
{"title":"A Machine Learning Approach to Human Activity Recognition","authors":"Umra Khan, S. Masood","doi":"10.1109/PDGC50313.2020.9315826","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315826","url":null,"abstract":"Human Activity Recognition (HAR) is the problem of classifying an individual's activity into well-defined moments, utilizing responsive sensors that are influenced by human movement. Sensor-enabled smartphones make Human Activity Recognition progressively significant and well known. The physical sensors, gyroscope and accelerometer combinedly allow the devices to provide motion measuring capabilities in a more accurate manner. The present research work adopts a machine learning based approach for recognizing activity on the basis of data collected through the smartphone sensors (accelerometer and gyroscope). Various state-of-the-art machine learning based techniques have been employed and compared on the basis of the performance metrics, accuracy, recall, precision, and the F1-score. Of all the selected different machine learning classifiers, the best result is given by the Support Vector Machine (SVM) with ‘RBF’ kernel, which achieved an accuracy of 96.61 % in classifying the activities into the six different classes.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121915847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315831
Sarita Jiyal, R. Saini
In all developing countries such as India the main problem of premature death is air pollution which also effect the economy of country. When urbanization started then various problem occurs such as environmental pollution, traffic system etc. there is so much loss of resources in crowded cities due to urbanization. The concept of smart sustainable city can be used to balance the resources. If we do loss of resources excessively than we will definitely create problems to our future generation and excessive use of resources causes air pollution. Than it is necessary to predict air pollution timely by which it can be monitored. Using Internet of Things monitoring of air pollution is necessary to save our environment from all harmful pollutants. Vehicles are the main cause of air pollution. Electric Vehicles and cycles can be used in place of other vehicles for controlling the air pollution. This research teaches that prediction of air pollution level is very important by which peoples can divert there route of travelling.
{"title":"Prediction and Monitoring of Air Pollution Using Internet of Things (IoT)","authors":"Sarita Jiyal, R. Saini","doi":"10.1109/PDGC50313.2020.9315831","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315831","url":null,"abstract":"In all developing countries such as India the main problem of premature death is air pollution which also effect the economy of country. When urbanization started then various problem occurs such as environmental pollution, traffic system etc. there is so much loss of resources in crowded cities due to urbanization. The concept of smart sustainable city can be used to balance the resources. If we do loss of resources excessively than we will definitely create problems to our future generation and excessive use of resources causes air pollution. Than it is necessary to predict air pollution timely by which it can be monitored. Using Internet of Things monitoring of air pollution is necessary to save our environment from all harmful pollutants. Vehicles are the main cause of air pollution. Electric Vehicles and cycles can be used in place of other vehicles for controlling the air pollution. This research teaches that prediction of air pollution level is very important by which peoples can divert there route of travelling.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129125183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315799
Amit Nayyer, A. Sharma, L. Awasthi
Software Defined Network is a significant and emerging paradigm that separates its control plane from the data plane. The separation of planes makes it centralized, different from the traditional network and provide various advantages to the network. The centralized paradigm offers a key benefit of global network view at the controller, which can be efficiently utilized for routing in the network. Along with benefits, there are several issues specific to routing that researchers need to address before developing a new routing protocol. The traditional routing protocols cannot be directly implemented in this modern architecture; if implemented, they cannot take full advantages of the paradigm. This article provided various issues of concern specifically for routing in Software Defined Networks. The target is to introduce newbies the issues and make them aware of multiple research efforts made in this direction. The discussion provided in the article can be considered before developing routing solutions for such networks.
{"title":"Issues with Routing in Software Defined Networks","authors":"Amit Nayyer, A. Sharma, L. Awasthi","doi":"10.1109/PDGC50313.2020.9315799","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315799","url":null,"abstract":"Software Defined Network is a significant and emerging paradigm that separates its control plane from the data plane. The separation of planes makes it centralized, different from the traditional network and provide various advantages to the network. The centralized paradigm offers a key benefit of global network view at the controller, which can be efficiently utilized for routing in the network. Along with benefits, there are several issues specific to routing that researchers need to address before developing a new routing protocol. The traditional routing protocols cannot be directly implemented in this modern architecture; if implemented, they cannot take full advantages of the paradigm. This article provided various issues of concern specifically for routing in Software Defined Networks. The target is to introduce newbies the issues and make them aware of multiple research efforts made in this direction. The discussion provided in the article can be considered before developing routing solutions for such networks.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129751431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315828
V. Shelake, N. Shekokar
Now-a-days, huge amount of personal and sensitive data of individuals resides across different data sources that refer to the same entity. Thus, it is crucial and necessary to detect and link duplicate records from multiple data sets in secure manner referred to as privacy preserving record linkage (PPRL). The PPRL enables data integration, analysis and research activities for business benefits. Since real world data exhibits its dirty and erroneous representations, achieving linkage accuracy is a prominent factor for PPRL techniques. Hence, approximate matching techniques play a crucial role for achieving linkage accuracy in PPRL applications. In this paper, different suitable attribute combinations for PPRL are identified. This paper introduces a similarity matching strategy for privacy preserving record linkage named as SMSPPRL for achieving increased linkage accuracy. Our SMSPPRL technique performs better than existing PPRL techniques Basic Bloom, hardened balanced Bloom filter in terms of linkage accuracy.
{"title":"SMSPPRL: A Similarity Matching Strategy for Privacy Preserving Record Linkage","authors":"V. Shelake, N. Shekokar","doi":"10.1109/PDGC50313.2020.9315828","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315828","url":null,"abstract":"Now-a-days, huge amount of personal and sensitive data of individuals resides across different data sources that refer to the same entity. Thus, it is crucial and necessary to detect and link duplicate records from multiple data sets in secure manner referred to as privacy preserving record linkage (PPRL). The PPRL enables data integration, analysis and research activities for business benefits. Since real world data exhibits its dirty and erroneous representations, achieving linkage accuracy is a prominent factor for PPRL techniques. Hence, approximate matching techniques play a crucial role for achieving linkage accuracy in PPRL applications. In this paper, different suitable attribute combinations for PPRL are identified. This paper introduces a similarity matching strategy for privacy preserving record linkage named as SMSPPRL for achieving increased linkage accuracy. Our SMSPPRL technique performs better than existing PPRL techniques Basic Bloom, hardened balanced Bloom filter in terms of linkage accuracy.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128035380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315738
M. Bharti, Himanshu Jindal
Social networking site Twitter, in particular, has become a popular spot for gossip. Rumors or false news spread very easily through the Twitter network by re-tweeting users without understanding the real truth. These reports trigger popular confusion, threaten the authority of the government and pose a major threat to social order. It is also a very necessary job to dispel theories as quickly as possible. In this research, multiple descriptive and consumer-based features via tweets are retrieved and integrated these features with the TF-IDF system to develop a composite set of features. This composite set of features is then used by several machine learning techniques like Support Vector Machine (SVM), Linear regression, K-Nearest Neighbor (KNN), Naive Bayes, Decision Tree, Random Forest, and Gradient Boosting. Along with these machine learning classification models, a Convolutional Neural Network (CNN) algorithm is proposed to distinguish rumour and non-rumor tweets. The proposed model is evaluated with freely accessible twitter datasets. The existing machine-based learning models have acquired an Fl-score of 0.46 to 0.76 for rumour detection, while the CNN model attained an Fl-score of 0.77 for rumour class. Overall, the CNN model yields greater results with a weighted average Fl-score of 0.84 for both rumour and non-rumor categories. The potential mechanism will help to detect misinformation as quickly as possible to counteract the dissemination of rumours and build users' deep confidence in social media sites.
{"title":"Automatic Rumour Detection Model on Social Media","authors":"M. Bharti, Himanshu Jindal","doi":"10.1109/PDGC50313.2020.9315738","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315738","url":null,"abstract":"Social networking site Twitter, in particular, has become a popular spot for gossip. Rumors or false news spread very easily through the Twitter network by re-tweeting users without understanding the real truth. These reports trigger popular confusion, threaten the authority of the government and pose a major threat to social order. It is also a very necessary job to dispel theories as quickly as possible. In this research, multiple descriptive and consumer-based features via tweets are retrieved and integrated these features with the TF-IDF system to develop a composite set of features. This composite set of features is then used by several machine learning techniques like Support Vector Machine (SVM), Linear regression, K-Nearest Neighbor (KNN), Naive Bayes, Decision Tree, Random Forest, and Gradient Boosting. Along with these machine learning classification models, a Convolutional Neural Network (CNN) algorithm is proposed to distinguish rumour and non-rumor tweets. The proposed model is evaluated with freely accessible twitter datasets. The existing machine-based learning models have acquired an Fl-score of 0.46 to 0.76 for rumour detection, while the CNN model attained an Fl-score of 0.77 for rumour class. Overall, the CNN model yields greater results with a weighted average Fl-score of 0.84 for both rumour and non-rumor categories. The potential mechanism will help to detect misinformation as quickly as possible to counteract the dissemination of rumours and build users' deep confidence in social media sites.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114626885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315762
Faisal Alam, Khan Saad Bin Hasan, Arpit Varshney
A large number of robots are used in warehouses to automate mundane tasks, reduce operating costs, make warehouses safer and more efficient. However, there is a tradeoff between cost and accuracy of the robot. A costly robot will be more accurate and precise in its working, But it cannot be used at a large scale in MSMEs in developing countries. Using cheap components would result in a lower cost but there will be a dip in accuracy. Having a low cost, fairly accurate robot would help in developing countries in MSMEs. We are building a low cost, autonomous robot that can assist us in transferring goods from one place to another within a storage facility which can also help us account for products. The robot must also be programmable to do multiple tasks if needed. In this work, We give a review of different robots currently being used in warehouses and explain the working of our robot. We also assess the cost and accuracy of our robot and show how it might be suitable for warehouses in developing countries.
{"title":"Low-Cost Autonomous Vehicle for Inventory Movement in Warehouses","authors":"Faisal Alam, Khan Saad Bin Hasan, Arpit Varshney","doi":"10.1109/PDGC50313.2020.9315762","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315762","url":null,"abstract":"A large number of robots are used in warehouses to automate mundane tasks, reduce operating costs, make warehouses safer and more efficient. However, there is a tradeoff between cost and accuracy of the robot. A costly robot will be more accurate and precise in its working, But it cannot be used at a large scale in MSMEs in developing countries. Using cheap components would result in a lower cost but there will be a dip in accuracy. Having a low cost, fairly accurate robot would help in developing countries in MSMEs. We are building a low cost, autonomous robot that can assist us in transferring goods from one place to another within a storage facility which can also help us account for products. The robot must also be programmable to do multiple tasks if needed. In this work, We give a review of different robots currently being used in warehouses and explain the working of our robot. We also assess the cost and accuracy of our robot and show how it might be suitable for warehouses in developing countries.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129270965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315795
A. Bansal, Aarushi Bhardwaj, Aman Sharma
Corona virus also known as COVID 19 is a critical ongoing pandemic that is on a rise across the globe. Italy and China have been considered as one of the main epicentres from where the pandemic came into full effect. Here, the highest death rates across the world are registered as a consequence of COVID-19. One of the leading countries, the USA has also been in the registered countries with an increasing number of cases of COVID 19. In this paper ARIMA model that is an auto regressive integrated moving average model is used to help forecast the epidemic trend over a period of time (i.e. April 2020). The dataset used is from the Italian epidemiological data at National and Regional level. It refers to the number of daily confirmed cases as well as the fatalities registered by Italian Ministry of Health. The model has various advantages like it is easy to use, to manage and a suitable model for forecasting purposes. Moreover, it gives a thorough clarity of basic trends, by predicting the hypothetical epidemic's inflection point and final size.
{"title":"Forecasting the Trend of Covid-19 Epidemic","authors":"A. Bansal, Aarushi Bhardwaj, Aman Sharma","doi":"10.1109/PDGC50313.2020.9315795","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315795","url":null,"abstract":"Corona virus also known as COVID 19 is a critical ongoing pandemic that is on a rise across the globe. Italy and China have been considered as one of the main epicentres from where the pandemic came into full effect. Here, the highest death rates across the world are registered as a consequence of COVID-19. One of the leading countries, the USA has also been in the registered countries with an increasing number of cases of COVID 19. In this paper ARIMA model that is an auto regressive integrated moving average model is used to help forecast the epidemic trend over a period of time (i.e. April 2020). The dataset used is from the Italian epidemiological data at National and Regional level. It refers to the number of daily confirmed cases as well as the fatalities registered by Italian Ministry of Health. The model has various advantages like it is easy to use, to manage and a suitable model for forecasting purposes. Moreover, it gives a thorough clarity of basic trends, by predicting the hypothetical epidemic's inflection point and final size.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"BME-17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132836835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315823
Rani Astya, N. Rakesh
Underwater Wireless Sensor network (UWSN) is a newly emerging area of wireless sensor network application which is used for naval, aquatic network, oiling network, surveillance, researchand distinct applicationinunderwater environment. Routing is one of the major concern of UWSN apart from mobility, bandwidth, robustness, high latency, node failure and various other. There are different research aspects which are categorized in variety of communication approaches in underwater environment which is quite different from the traditional approaches of network communication. In this paper we have broadly classifiedmost of the existing routing protocols in accordance to the usability. The classification is defined based on data forwarding and operations of routing protocols. This paper has distinguished the routing mechanisms to be adopted in accordance to the application requirement of Underwater Wireless Sensors for dynamic and static applicability.
{"title":"Classification of Routing Protocols for Under Water Sensor Network","authors":"Rani Astya, N. Rakesh","doi":"10.1109/PDGC50313.2020.9315823","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315823","url":null,"abstract":"Underwater Wireless Sensor network (UWSN) is a newly emerging area of wireless sensor network application which is used for naval, aquatic network, oiling network, surveillance, researchand distinct applicationinunderwater environment. Routing is one of the major concern of UWSN apart from mobility, bandwidth, robustness, high latency, node failure and various other. There are different research aspects which are categorized in variety of communication approaches in underwater environment which is quite different from the traditional approaches of network communication. In this paper we have broadly classifiedmost of the existing routing protocols in accordance to the usability. The classification is defined based on data forwarding and operations of routing protocols. This paper has distinguished the routing mechanisms to be adopted in accordance to the application requirement of Underwater Wireless Sensors for dynamic and static applicability.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132139456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315848
Nishant Agnihotri, A. Sharma
Lately the trend of the internet is taking a front seat for different applications. Organizations are collecting and processing and then sharing the data using the internet. Sharing using public network will invite various security lapses in the data. Security will remain the maj or thrust in the area for providing enough level of security for the data. Encryption is the best way to provide security for the data. There are two different types of approaches for ensuring data security. These techniques are symmetric and asymmetric. The symmetric technique includes different approaches with variation in the time and space complexity. In this research paper five different techniques of the symmetric approaches are compared for three different length strings. AES is the best performing in all the three cases. The time comparison for the AES with different techniques is comparatively better than the other four techniques like IDEA, RC6, Two Fish, MARS.
{"title":"Comparative Analysis of Different Symmetric Encryption Techniques Based on Computation Time","authors":"Nishant Agnihotri, A. Sharma","doi":"10.1109/PDGC50313.2020.9315848","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315848","url":null,"abstract":"Lately the trend of the internet is taking a front seat for different applications. Organizations are collecting and processing and then sharing the data using the internet. Sharing using public network will invite various security lapses in the data. Security will remain the maj or thrust in the area for providing enough level of security for the data. Encryption is the best way to provide security for the data. There are two different types of approaches for ensuring data security. These techniques are symmetric and asymmetric. The symmetric technique includes different approaches with variation in the time and space complexity. In this research paper five different techniques of the symmetric approaches are compared for three different length strings. AES is the best performing in all the three cases. The time comparison for the AES with different techniques is comparatively better than the other four techniques like IDEA, RC6, Two Fish, MARS.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124856644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315735
Samarth Garg, Divyansh Singh Panwar, Aakansha Gupta, R. Katarya
Sentiment analysis refers to the active field of Natural language processing that extracts the attitude and emotion of a human being. With the growth of social media, more people are using online platforms such as Twitter, Facebook, Y ouTube, etc. to express their opinions. Twitter is considered to be the purest platform to express one's views. Mostly all personalities from diverse backgrounds use twitter. Therefore, it becomes a need of the hour to study public opinion. This provides us valuable information and helps organizations and governments to contemplate mass public opinion and take better decisions accordingly. In this review paper, an extensive and exhaustive guide to the subfield of Natural language processing (NLP), focusing precisely on sentiment analysis on twitter dataset, has been presented. It highlights three main approaches to analyze the sentiment. We have summarized and compared the approaches on different metrics opted by various researchers in the field of sentiment analysis using the twitter dataset. With so much active work in this field, this review paper would assist all future researchers.
{"title":"A Literature Review On Sentiment Analysis Techniques Involving Social Media Platforms","authors":"Samarth Garg, Divyansh Singh Panwar, Aakansha Gupta, R. Katarya","doi":"10.1109/PDGC50313.2020.9315735","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315735","url":null,"abstract":"Sentiment analysis refers to the active field of Natural language processing that extracts the attitude and emotion of a human being. With the growth of social media, more people are using online platforms such as Twitter, Facebook, Y ouTube, etc. to express their opinions. Twitter is considered to be the purest platform to express one's views. Mostly all personalities from diverse backgrounds use twitter. Therefore, it becomes a need of the hour to study public opinion. This provides us valuable information and helps organizations and governments to contemplate mass public opinion and take better decisions accordingly. In this review paper, an extensive and exhaustive guide to the subfield of Natural language processing (NLP), focusing precisely on sentiment analysis on twitter dataset, has been presented. It highlights three main approaches to analyze the sentiment. We have summarized and compared the approaches on different metrics opted by various researchers in the field of sentiment analysis using the twitter dataset. With so much active work in this field, this review paper would assist all future researchers.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"266 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127544732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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