scholarly journals Sentiment Analysis using Ensemble Classifier on Real Time Data Set

2020 ◽  
Vol 8 (2) ◽  
pp. 347-351
Author(s):  
Aman Goenka
Keyword(s):  
Sentiment Analysis ◽  
Real Time ◽  
Ensemble Classifier ◽  
Time Data ◽  
Data Set ◽  
Real Time Data

scholarly journals Literature review on Real-time Location-Based Sentiment Analysis on Twitter

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Dilmini Rathnayaka ◽  
Pubudu K.P.N Jayasena ◽  
Iraj Ratnayake
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Sentiment Analysis ◽  
Real Time ◽  
Analysis Data ◽  
Time Data ◽  
Twitter Data ◽  
Analysis Methods ◽  
Real Time Data ◽  
Twitter Data Analysis

Sentiment analysis mainly supports sorting out the polarity and provides valuable information with the use of raw data in social media platforms. Many fields like health, business, and security require real-time data analysis for instant decision-making situations.Since Twitter is considered a popular social media platform to collect data easily, this paper is considering data analysis methods of Twitter data, real-time Twitter data analysis based on geo-location. Twitter data classification and analysis can be done with the use of diverse algorithms and deciding the most appropriate algorithm for data analysis, can be accomplished by implementing and testing these diverse algorithms.This paper is discussing the major description of sentiment analysis, data collection methods, data pre-processing, feature extraction, and sentiment analysis methods related to Twitter data. Real-time data analysis arises as a major method of analyzing the data available online and the real-time Twitter data analysis process is described throughout this paper. Several methods of classifying the polarized Twitter data are discussed within the paper while depicting a proposed method of Twitter data analyzing algorithm. Location-based Twitter data analysis is another crucial aspect of sentiment analyses, that enables data sorting according to geo-location, and this paper describes the way of analyzing Twitter data based on geo-location. Further, a comparison about several sentiment analysis algorithms used by previous researchers has been reported and finally, a conclusion has been provided.

scholarly journals Scalable and Reliable Deep Learning Model to handle real-time Streaming Data

2020 ◽  
Vol 9 (3) ◽  
pp. 3840-3844 ◽  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Data Streams ◽  
Learning Algorithm ◽  
Streaming Data ◽  
Experimental Result ◽  
Time Data ◽  
Data Set ◽  
Public Data ◽  
Real Time Data

We have real-time data everywhere and every day. Most of the data comes from IoT sensors, data from GPS positions, web transactions and social media updates. Real time data is typically generated in a continuous fashion. Such real-time data are called Data streams. Data streams are transient and there is very little time to process each item in the stream. It is a great challenge to do analytics on rapidly flowing high velocity data. Another issue is the percentage of incoming data that is considered for analytics. Higher the percentage greater would be the accuracy. Considering these two issues, the proposed work is intended to find a better solution by gaining insight on real-time streaming data with minimum response time and greater accuracy. This paper combines the two technology giants TensorFlow and Apache Kafka. is used to handle the real-time streaming data since TensorFlow supports analytics support with deep learning algorithms. The Training and Testing is done on Uber connected vehicle public data set RideAustin. The experimental result of RideAustin shows the predicted failure under each type of vehicle parameter. The comparative analysis showed 16% improvement over the traditional Machine Learning algorithm.

scholarly journals Smart Transit Dynamic Optimization and Informatics

2021 ◽  
Author(s):  
Hamid Reza Sabarshad
Keyword(s):  
New York ◽  
Social Welfare ◽  
Real Time ◽  
Dynamic Optimization ◽  
Arrival Process ◽  
Time Data ◽  
Data Set ◽  
Transit System ◽  
Real Time Data ◽  
Future Demand

With the popularity of Big Data and urban informatics, there is increasing interest in ways to use real time data to improve transportation system operations. In many real-wold applications, demand is revealed dynamically over time, and consequently the routes are determined dynamically as well. In this thesis, contributions are made to several key components of a “smart” transit system framework where dynamic operations are driven by real time information. The first component is in dynamic routing and pricing of a fleet of vehicles. A new dynamic dial-a-ride policy is introduced that features non-myopic pricing based on optimal tolling of queues to fit with the multi-server queueing approximation method. By including social optimal pricing, the social welfare of the resulting system outperforms a pricing policy based on the marginal cost increase of a passenger over a range of test instances. In the examples tested, improvements in social welfare of the non-myopic pricing over the myopic pricing were in the 20% - 31% range. The second component is in the informatics. Effective dynamic optimization of a system (routing, scheduling, fare setting, etc.) requires effective short term prediction of traveler/customer arrival using real-time data. Several recent methods for arrival process prediction, both offline and online, are investigated using real taxi data from New York. An experiment is conducted using the same data set to draw comparisons for arrival process modeling, suggesting that the temporal seasonal factors method from Ihlers et al. (2006) is more effective as an offline approach and the IntGARCH method from Matteson et al. (2011) is more effective as an online approach. The third component investigated is in the prepositioning of idle vehicles. Vehicles that are positioned at locations that take into account future demand can lead to reduced wait times for passengers and improved level of service. A dynamic relocation model is proposed that includes queueing delay to approximate the congestion effect of future demand. A linear problem is formulated based on Marianov and Serra’s (2002) work. By varying customer arrivals, the approach provides a new managerial tool to find the optimal service level.

scholarly journals Sentiment Classification System of Twitter Data using Python

2020 ◽  
Vol 8 (6) ◽  
pp. 1042-1044
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Real Time ◽  
Classification System ◽  
Sentiment Classification ◽  
Time Data ◽  
Twitter Data ◽  
Real Time Data ◽  
Share Data ◽  
Day By Day

Social media has developed drastically over the years. These days, individuals from all around the globe utilize online networking destinations to share data and information. Twitter is a well known communication site where users update information or messages known as tweets. Users share their day by day lives, post their opinions on everything, for example, brands and places. Various purchasers and advertisers utilize these tweets to accumulate bits of knowledge of their items and opinions on them. The aim of this paper is to exhibit a model that can perform sentiment analysis of real-time data collected from twitter and classify the tweets into positive, negative or neutral based on the sentiment expressed in them.

Sign in / Sign up

Export Citation Format

Share Document