scholarly journals A. Amalgamative sentiment analysis framework on social networking site

2019 ◽  
Vol 1228 ◽  
pp. 012010 ◽  
Author(s):  
M ArunaSafali ◽  
R Satya Prasad ◽  
KBS Sastry
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Site ◽  
Analysis Framework

scholarly journals Leveraging Cloud Computing and Microservices for Tweet Analysis

2021 ◽  
Vol 10 (11) ◽  
pp. 12-18
Author(s):  
Manoj Kumar Malik ◽  
Harsh S Malkani ◽  
Prinshu Dahiya
Keyword(s):  
Cloud Computing ◽  
Online Learning ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Site ◽  
Important Data ◽  
The Real

Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking site like Twitter is a widely used platform where millions of tweets are tweeted every day and most of these tweets never reach their intended audiences and fail fulfill their purposes because they are lost in a huge sea of tweets that are often irrelevant. Analysis of a segment of tweets might not truly reflect the real sentiments of the overall tweets on a topic which is a challenge. To tackle this problem, we introduce efficient techniques with which tweets are extracted, translated and sentiment analysis is performed on both text and images. These results are shown graphically and tabularly with other useful and important data such as username and hashtags used in tweets. For controlled access and security, login and registration features are incorporated.

A Methodology to Handle Heterogeneous Data Generated in Online Social Networks

2020 ◽  
Vol 17 (9) ◽  
pp. 4098-4102
Author(s):  
K. Sailaja Kumar ◽  
D. Evangelin Geetha ◽  
Pratap Rudra Sahoo
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Heterogeneous Data ◽  
Social Networking Site ◽  
Stream Data ◽  
Text Data ◽  
Research Challenge ◽  
Python Programming

Analyzing the heterogeneous data generated by social networking sites is a research challenge. Twitter is a massive social networking site. In this paper, for processing the heterogeneous data, a methodology is devised, which helps in categorizing the data obtained from Twitter into different directories and understanding the text data explicitly. The methodology is implemented using Python programming language. Python’s tweepy package is used to download the Twitter stream data which includes images, videos and text data. Python’s Aylien API is used for analyzing the Twitter text data. Using this API, sentiment analysis report is generated. Using Python’s matplotlib package, a pie chart is generated to visualize the sentiment analysis results. Further an algorithm is proposed for sentiment analysis, which not only categorizes the tweets into positive, negative and neutral (as Aylien API does), but also categorizes the tweets into strongly and weakly, positive and negative based on the polarity and subjectivity. Django platform and Python’s TextBlob package are used for implementing this algorithm. For this experiment, data is collected from Twitter using the hash tags related to different events/topics like IPL2018, World Cup2018, Modi, and Delete Facebook etc. during the period Monday Jan 22, 2018 to Monday May 28, 2018. Moreover, the data is collected and processed using Python TextBlob. Also conducted the Sentiment analysis on text data using TextBlob and visual reports are generated using Google chart. The results obtained from both the above-mentioned approaches are compared and it is observed that the proposed algorithm gives better sentiment analysis of the tweets.

scholarly journals Assorted Model of Sentiment using Mapreduce Framework

2020 ◽  
Vol 8 (6) ◽  
pp. 2193-2203
Keyword(s):  
Parallel Processing ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Parallel Programming ◽  
Social Networking Site ◽  
Programming Models ◽  
Training Algorithm ◽  
Mapreduce Framework ◽  
Single Node ◽  
Parallel Programming Models

Social networking sites platforms, such as Facebook and Twitter, are being broadly used by community to share their feelings on different matters. Consequently, social networking site becomes an admirable and major open source for collecting public opinion. To perform sentiment analysis on such huge data, computational assorted models of single node are ineffective. Two ways to grip data that are big ,either by using super computers or by using parallel processing or by distributed processing. Where it is costly to use super computer, most models of parallel processing such as MPI, are difficult to implement and scaling, MapReduce is one of the parallel processing models that is highly scalable, tolerant to fault, and easy for using, in this research paper, we have proposed assorted model of sentiment analysis for twitter using MapReduce Framework. mapreduce based naïve bayes training algorithm was proposed for this purpose. Only single mapreduce job is executed for this algorithm which makes it different from earlier previous work. Training model is deployed to to classify million of tweets of twitter computers are costly, most parallel programming models, such as MPI, are difficult to use and scale. MapReduce is one of the parallel programming models that is highly scalable, fault tolerant and easy to use. This paper proposes a scalable framework for sentiment analysis of Twitter using MapReduce model. For this purpose a MapReduce based Na¨ıve Bayes training algorithm is proposed, this algorithm uses only one MapReduce job which makes it different from previous works. The trained model is deployed to classify millions of tweets. Accuracy and Scalabilty of our propsed model is well compared to previous models.

scholarly journals Multilingual twitter sentiment analysis using machine learning

2020 ◽  
Vol 10 (6) ◽  
pp. 5992
Author(s):  
K. Arun ◽  
A. Srinagesh
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Good Accuracy ◽  
Processing Method ◽  
Social Networking Site ◽  
Data Sparsity ◽  
Research Fields ◽  
Twitter Data ◽  
The World

Twitter sentiment analysis is one of the leading research fields. Most of the researchers were contributed to twitter sentiment analysis in English tweets, but few researchers focus on the multilingual twitter sentiment analysis. Some challenges are hoping for the research solutions in multilingual twitter sentiment analysis. This study presents the implementation of sentiment analysis in multilingual twitter data and improves the data classification up to the adequate level of accuracy. Twitter is the sixth leading social networking site in the world. Active users for twitter in a month are 330 million. People can tweet or re-tweet in their languages and allow users to use emoji’s, abbreviations, contraction words, miss spellings, and shortcut words. The best platform for sentiment analysis is twitter. Multilingual tweets and data sparsity are the two main challenges. In this paper, the MLTSA algorithm gives the solution for these two challenges. MLTSA algorithm divides into two parts. One is detecting and translating non-English tweets into English using natural language processing (NLP). And the second one is an appropriate pre-processing method with NLP support can reduce the data sparsity. The result of the MLTSA with SVM achieves good accuracy by up to 95%.

Effects of Body Image on Internet Social Networking Site Self-Disclosure

2012 ◽  
Author(s):  
Lila M. Inglima ◽  
Jason C. Zeltser ◽  
Eric Schmidt ◽  
M. Blair Chinn ◽  
Katherine Price ◽  
...  
Keyword(s):  
Body Image ◽  
Social Networking ◽  
Social Networking Site ◽  
Self Disclosure

Design and Implementation of a Social Networking Site

2014 ◽  
Vol 9 (2) ◽  
pp. 1-8
Author(s):  
Hana Esmaeel ◽  
Mustafa Laith Hussein ◽  
Afkar Abdul-Ellah ◽  
Abdul Jabbar
Keyword(s):  
Social Networking ◽  
Social Networking Site ◽  
Design And Implementation

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

Sign in / Sign up

Export Citation Format

Share Document