Sarcasm Detection in Twitter Data

2019 ◽  
pp. 246-272
Author(s):  
Santosh Kumar Bharti ◽  
Sathya Babu Korra
Keyword(s):  
Social Media ◽  
State Of The Art ◽  
Historical Context ◽  
Experimental Results ◽  
Automated System ◽  
Business Organization ◽  
Twitter Data ◽  
Contextual Feature ◽  
The Social ◽  
Social Media Text

Posting sarcastic messages on social media like Twitter, Facebook, WhatsApp, etc., became a new trend to avoid direct negativity. Detecting this indirect negativity in the social media text has become an important task as they influence every business organization. In the presence of sarcasm, detection of actual sentiment on these texts has become the most challenging task. An automated system is required that will be capable of identifying actual sentiment of a given text in the presence of sarcasm. In this chapter, we proposed an automated system for sarcasm detection in social media text using six algorithms that are capable to analyze the various types of sarcasm occurs in Twitter data. These algorithms use lexical, pragmatic, hyperbolic and contextual features of text to identify sarcasm. In the contextual feature, we mainly focus on situation, topical, temporal, and historical context of the text. The experimental results of proposed approach were compared with state-of-the-art techniques.

Spam Detection on Social Media Using Semantic Convolutional Neural Network

2018 ◽  
Vol 8 (1) ◽  
pp. 12-26 ◽  
Author(s):  
Gauri Jain ◽  
Manisha Sharma ◽  
Basant Agarwal
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Spam Detection ◽  
Learning Technology ◽  
The Social ◽  
Social Media Text ◽  
Current Article ◽  
Semantic Layer

This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.

scholarly journals Sentiment analysis of Social Media Text-Emoticon Post with Machine learning Models Contribution Title

2021 ◽  
Vol 2070 (1) ◽  
pp. 012079
Author(s):  
V Jagadishwari ◽  
A Indulekha ◽  
Kiran Raghu ◽  
P Harshini
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Data Sets ◽  
Learning Models ◽  
Twitter Data ◽  
The Social ◽  
Social Media Text ◽  
Machine Learning Models

Abstract Social Media is an arena in recent times for people to share their perspectives on a variety of topics. Most of the social interactions are through the Social Media. Though all the Online Social Networks allow users to express their views and opinions in many forms like audio, video, text etc, the most popular form of expression is text, Emoticons and Emojis. The work presented in this paper aims at detecting the sentiments expressed in the Social Media posts. The Machine Learning Models namely Bernoulli Bayes, Multinomial Bayes, Regression and SVM were implemented. All these models were trained and tested with Twitter Data sets. Users on Twitter express their opinions in the form of tweets with limited characters. Tweets also contain Emoticons and Emojis therefore Twitter data sets are best suited for the sentiment analysis. The effect of emoticons present in the tweet is also analyzed. The models are first trained only with the text and then they are trained with text and emoticon in the tweet. The performance of all the four models in both cases are tested and the results are presented in the paper.

On the Privacy and Utility of Anonymized Social Networks

2013 ◽  
Vol 4 (2) ◽  
pp. 1-34 ◽  
Author(s):  
Yi Song ◽  
Xuesong Lu ◽  
Sadegh Nobari ◽  
Stéphane Bressan ◽  
Panagiotis Karras
Keyword(s):  
Social Networks ◽  
Social Media ◽  
State Of The Art ◽  
Social Groups ◽  
Trade Off ◽  
Private Lives ◽  
The Social ◽  
Before And After ◽  
Do So

One is either on Facebook or not. Of course, this assessment is controversial and its rationale arguable. It is nevertheless not far, for many, from the reason behind joining social media and publishing and sharing details of their professional and private lives. Not only the personal details that may be revealed, but also the structure of the networks are sources of invaluable information for any organization wanting to understand and learn about social groups, their dynamics and members. These organizations may or may not be benevolent. It is important to devise, design and evaluate solutions that guarantee some privacy. One approach that reconciles the different stakeholders’ requirement is the publication of a modified graph. The perturbation is hoped to be sufficient to protect members’ privacy while it maintains sufficient utility for analysts wanting to study the social media as a whole. In this paper, the authors try to empirically quantify the inevitable trade-off between utility and privacy. They do so for two state-of-the-art graph anonymization algorithms that protect against most structural attacks, the k-automorphism algorithm and the k-degree anonymity algorithm. The authors measure several metrics for a series of real graphs from various social media before and after their anonymization under various settings.

An Approach to Distinguish Between the Severity of Bullying in Messages in Social Media

2016 ◽  
Vol 3 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Geetika Sarna ◽  
M.P.S. Bhatia
Keyword(s):  
Social Media ◽  
Preventive Measures ◽  
State Of The Art ◽  
Negative Information ◽  
Security And Privacy ◽  
Multimedia Content ◽  
The Real ◽  
The Social

Users on the social media can share positive as well as negative information intentionally and unintentionally in the form of multimedia content without knowing its impact on other user, which threatens the security and privacy of social media. Cyberbullying is one of the risks associated with social media. Cyberbullying is an aggressive act carried out intentionally against the victim by posting harmful material on social media to harm his/her reputation. Aggressive act creates depression, anxiety in users which may lead to diversion of attention and sometimes suicidal actions. In this paper the authors have included a survey on recent algorithms which work on detection of cyberbullying. State-of-the-art studies only focus on the detection of cyberbullying but not on the preventive measures against cyberbullying. In order to tackle this problem, the authors showed how the severity of the bullying in messages helps to find the real culprit.

‘Baptising Zambia’s Edgar Chagwa Lungu’: Critiquing the Utilization of the Declaration of Zambia during the Presidential Campaign of 2016

2019 ◽  
Vol 13 (1) ◽  
pp. 72-93
Author(s):  
Chammah J. Kaunda
Keyword(s):  
Social Media ◽  
Thematic Analysis ◽  
Social Practice ◽  
Historical Context ◽  
Moral Authority ◽  
Presidential Campaign ◽  
Public Theology ◽  
Decolonial Theory ◽  
The Social ◽  
Christian Nation

AbstractThis article employs a public theology approach from the perspective of a decolonial theory. It analyses how the Declaration of Zambia as a Christian Nation functioned as a nationalist neo-colonial ideology during the presidential campaign of 2016. It did so in a way that was designed to legitimize President Edgar Chagwa Lungu’s political candidacy and moral authority within the Pentecostal-Charismatic religious sector. The analysis seeks to demonstrate how the Declaration and the photography of the social media presidential campaign intersected in order to represent the image of Lungu as an idea Christian President. Informed by a thematic analysis and a decolonial public theology, the article unmasks and exposes how ideology can become normalized as social practice within a particular historical context. The theological-ethnographic material within the analysis was collected during the period from January 2016 to February 2017.

scholarly journals Visual Sentiment Analysis on Social Media Data

2021 ◽  
pp. 366-372
Author(s):  
Harshala Bhoir ◽  
K. Jayamalini
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Social Media Data ◽  
Media Image ◽  
The Social ◽  
Textual Data ◽  
Art Works ◽  
Negative Sentiment ◽  
Media Data

Visual sentiment analysis is the way to automatically recognize positive and negative emotions from images, videos, graphics, stickers etc. To estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment, most of the state-of-the-art works exploit the text associated to a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user which usually includes text useful to maximize the diffusion of the social post. Proposed system will extract and employ an Objective Text description of images automatically extracted from the visual content rather than the classic Subjective Text provided by the user. The proposed System will extract three views visual view, subjective text view and objective text view of social media image and will give sentiment polarity positive, negative or neutral based on hypothesis table.

scholarly journals Sentiment Analysis Of Twitter Data By Using Deep Learning And Machine Learning

2021 ◽  
Vol 12 (6) ◽  
pp. 962-970
Author(s):  
Prof. Manisha Sachin Dabade, Et. al.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Daily Monitoring ◽  
Fine Grain ◽  
Large Size ◽  
Twitter Data ◽  
The Social ◽  
Enormous Amount

In today’s world, social media is viral and easily accessible. The Social media sites like Twitter, Facebook, Tumblr, etc. are a primary and valuable source of information.Twitter is a micro-blogging platform, and it provides an enormous amount of data. Such type of information can use for different sentiment analysis applications such as reviews, predictions, elections, marketing, etc. It is one of the most popular sites where peoples write tweets, retweets, and interact daily. Monitoring and analyzing these tweets give valuable feedback to users. Due to this data's large size, sentiment analysis is using to analyze this data without going through millions of tweets manually. Any user writes their reviews about different products, topics, or events on Twitter, called tweets and retweets. People also use emojis such as happy, sad, and neutral in expressing their emotions, so these sites contain expansive volumes of unprocessed data called raw data. The main goal of this research is to recognize the algorithms by using Machine Learning Classifiers. The study intends to categorize Fine-grain sentiments within Tweets of Vaccination (89974 tweets) through machine learning and a deep learning approach. The study takes consideration of both labeled and unlabeled data. It also detects emojis from tweets using machine learning libraries like Textblob, Vadar, Fast text, Flair, Genism, spaCy, and NLTK.

scholarly journals WAJAH “LAMAHOLOT CYBER-SELF”: CATATAN ETNOGRAFIS VIRTUAL ATAS PRESENTASI DIRI ANGGOTA GRUP FACEBOOK “SUARA FLOTIM”

2020 ◽  
Author(s):  
Aurelius RL Teluma ◽  
Rini Kartini
Keyword(s):  
Social Media ◽  
The Self ◽  
Young Generation ◽  
Cultural Interaction ◽  
Ethnographic Methods ◽  
Cultural Economic ◽  
Self Presentation ◽  
The Social ◽  
Social Media Text ◽  
Political Dimensions

Social media is a socio-technological entity. Social media text is a representation of the social, cultural, economic and political dimensions of its users. When social media was known and used by the young generation of the Lamaholot people, there was a cultural interaction with the Lamaholot Lika Telo kinship culture which gave birth to identity and Lamaholot-cyber self. This research is textual-contextual research using virtual ethnographic methods that aim to identify and describe the general characteristics of Lamaholot-cyber social media characteristics. The subjects examined are Lamaholot Facebook users who are members of a Facebook group "Suara Flotim". The virtual ethnographic observations of the self-presentation of Lamaholot Facebookers showed that the cyber Lamaholot was a self and a discursive identity even to be paradoxical entity.

scholarly journals Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search

2019 ◽  
Vol 33 ◽  
pp. 232-240 ◽  
Author(s):  
Jinfeng Rao ◽  
Wei Yang ◽  
Yuhao Zhang ◽  
Ferhan Ture ◽  
Jimmy Lin
Keyword(s):  
Social Media ◽  
Ad Hoc ◽  
Similarity Measurement ◽  
Web Pages ◽  
Ranking Models ◽  
Twitter Data ◽  
The Social ◽  
Ad Hoc Retrieval ◽  
Feature Based ◽  
Substantial Interest

Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have mostly been applied to “standard” ad hoc retrieval tasks over web pages and newswire articles. This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network), a novel neural ranking model specifically designed for ranking short social media posts. We identify document length, informal language, and heterogeneous relevance signals as features that distinguish documents in our domain, and present a model specifically designed with these characteristics in mind. Our model uses hierarchical convolutional layers to learn latent semantic soft-match relevance signals at the character, word, and phrase levels. A poolingbased similarity measurement layer integrates evidence from multiple types of matches between the query, the social media post, as well as URLs contained in the post. Extensive experiments using Twitter data from the TREC Microblog Tracks 2011–2014 show that our model significantly outperforms prior feature-based as well as existing neural ranking models. To our best knowledge, this paper presents the first substantial work tackling search over social media posts using neural ranking models. Our code and data are publicly available.1

scholarly journals SPATIAL-TEMPORAL ANALYSIS OF SOCIAL MEDIA DATA RELATED TO NEPAL EARTHQUAKE 2015

2016 ◽  
Vol XLI-B2 ◽  
pp. 567-571
Author(s):  
L. Thapa
Keyword(s):  
Social Media ◽  
Temporal Analysis ◽  
Archaeological Sites ◽  
Social Media Data ◽  
User Input ◽  
Nepal Earthquake ◽  
Twitter Data ◽  
The Social ◽  
Media Data ◽  
Spatial Temporal Analysis

Social Medias these days have become the instant communication platform to share anything; from personal feelings to the matter of public concern, these are the easiest and aphoristic way to deliver information among the mass. With the development of Web 2.0 technologies, more and more emphasis has been given to user input in the web; the concept of Geoweb is being visualized and in the recent years, social media like Twitter, Flicker are among the popular Location Based Social Medias with locational functionality enabled in them. Nepal faced devastating earthquake on 25 April, 2015 resulting in the loss of thousands of lives, destruction in the historical-archaeological sites and properties. Instant help was offered by many countries around the globe and even lots of NGOs, INGOs and people started the rescue operations immediately; concerned authorities and people used different communication medium like Frequency Modulation Stations, Television, and Social Medias over the World Wide Web to gather information associated with the Quake and to ease the rescue activities. They also initiated campaign in the Social Media to raise the funds and support the victims. Even the social medias like Facebook, Twitter, themselves announced the helping campaign to rebuild Nepal. In such scenario, this paper features the analysis of Twitter data containing hashtag related to Nepal Earthquake 2015 together with their temporal characteristics, when were the message generated, where were these from and how these spread spatially over the internet?

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