Computer Engineering Commons^™

Sentiment Analysis Of Text And Emoji Data For Twitter Network, Paramita Dey, Soumya Dey

Al-Bahir Journal for Engineering and Pure Sciences

Twitter is a social media platform where users can post, read, and interact with 'tweets'. Third party like corporate organization can take advantage of this huge information by collecting data about their customers' opinions. The use of emoticons on social media and the emotions expressed through them are the subjects of this research paper. The purpose of this paper is to present a model for analyzing emotional responses to real-life Twitter data. The proposed model is based on supervised machine learning algorithms and data on has been collected through crawler “TWEEPY” for empirical analysis. Collected data is pre-processed, pruned and …

Medical Concept Mention Identification In Social Media Posts Using A Small Number Of Sample References, Vasudevan Nedumpozhimana, Sneha Rautmare, Meegan Gower, Maja Popovic, Nishtha Jain, Patricia Buffini, John Kelleher

Conference papers

Identification of mentions of medical concepts in social media text can provide useful information for caseload prediction of diseases like Covid-19 and Measles. We propose a simple model for the automatic identification of the medical concept mentions in the social media text. We validate the effectiveness of the proposed model on Twitter, Reddit, and News/Media datasets.

Evaluation Of Social Bot Detection Models, Muhammet Buğra Torusdağ, Mücahi̇d Kutlu, Ali̇ Aydin Selçuk

Turkish Journal of Electrical Engineering and Computer Sciences

Social bots are employed to automatically perform online social network activities; thereby, they can also be utilized in spreading misinformation and malware. Therefore, many researchers have focused on the automatic detection of social bots to reduce their negative impact on society. However, it is challenging to evaluate and compare existing studies due to difficulties and limitations in sharing datasets and models. In this study, we conduct a comparative study and evaluate four different bot detection systems in various settings using 20 different public datasets. We show that high-quality datasets covering various social bots are critical for a reliable evaluation of …

Twitter Account Classification Using Account Metadata: Organizationvs. Individual, Yusuf Mucahi̇t Çeti̇nkaya, Mesut Gürlek, İsmai̇l Hakki Toroslu, Pinar Karagöz

Turkish Journal of Electrical Engineering and Computer Sciences

Organizations present their existence on social media to gain followers and reach out to the crowds. Social media-related tasks and applications, such as social media graph construction, sentiment analysis, and bot detection, are required to identify the entities' account types. Some applications focus on personal accounts, whereas others only need nonpersonal accounts. This paper addresses the account classification problem using only minimum amount of data, which is the metadata of the account's profile. The proposed approach classifies accounts either as organization or individual, in a language-independent manner, without collecting the accounts' tweet content. The model uses a long short term …

Data-Driven Studies On Social Networks: Privacy And Simulation, Yasanka Sameera Horawalavithana

USF Tampa Graduate Theses and Dissertations

Social media datasets are fundamental to understanding a variety of phenomena, such as epidemics, adoption of behavior, crowd management, and political uprisings. At the same time, many such datasets capturing computer-mediated social interactions are recorded nowadays by individual researchers or by organizations. However, while the need for real social graphs and the supply of such datasets are well established, the flow of data from data owners to researchers is significantly hampered by privacy risks: even when humans’ identities are removed, or data is anonymized to some extent, studies have proven repeatedly that re-identifying anonymized user identities (i.e., de-anonymization) is doable …

Exploiting Bert And Roberta To Improve Performance For Aspect Based Sentiment Analysis, Gagan Reddy Narayanaswamy

Dissertations

Sentiment Analysis also known as opinion mining is a type of text research that analyses people’s opinions expressed in written language. Sentiment analysis brings together various research areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is fast becoming of major importance to companies and organizations as it is started to incorporate online commerce data for analysis. Often the data on which sentiment analysis is performed will be reviews. The data can range from reviews of a small product to a big multinational corporation. The goal of performing sentiment analysis is to extract information from those …

Finetuning Bert And Xlnet For Sentiment Analysis Of Stock Market Tweets Using Mixout And Dropout Regularization, Shubham Jangir

Dissertations

Sentiment analysis is also known as Opinion mining or emotional mining which aims to identify the way in which sentiments are expressed in text and written data. Sentiment analysis combines different study areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is quickly becoming a key concern for businesses and organizations, especially as online commerce data is being used for analysis. Twitter is also becoming a popular microblogging and social networking platform today for information among people as they contribute their opinions, thoughts, and attitudes on social media platforms over the years. Because of the large …

Twitter Analysis Based On Damage Detection And Geoparsing For Event Mapping Management, Yasmeen Ali, Khaled Bahnasy, Adel El-Mahdy

Future Computing and Informatics Journal

Background: Early event detection, monitor, and response can significantly decrease the impact of disasters. Lately, the usage of social media for detecting events has displayed hopeful results. Objectives: for event detection and mapping; the tweets will locate and monitor them on a map. This new approach uses grouped geoparsing then scoring for each tweet based on three spatial indicators. Method/Approach: Our approach uses a geoparsing technique to match a location in tweets to geographic locations of multiple-events tweets in Egypt country, administrative subdivision. Thus, additional geographic information acquired from the tweet itself to detect the actual locations that …

Identifying Depressive Symptoms From Tweets: Figurative Language Enabled Multitask Learning Framework, Shweta Yadav, Jainish Chauhan, Joy Prakash Sain, Krishnaprasad Thirunarayan, Amit P. Sheth, Jeremiah Schumm

Publications

Existing studies on using social media for deriving mental health status of users focus on the depression detection task. However, for case management and referral to psychiatrists, healthcare workers require practical and scalable depressive disorder screening and triage system. This study aims to design and evaluate a decision support system (DSS) to reliably determine the depressive triage level by capturing fine-grained depressive symptoms expressed in user tweets through the emulation of Patient Health Questionnaire-9 (PHQ-9) that is routinely used in clinical practice. The reliable detection of depressive symptoms from tweets is challenging because the 280-character limit on tweets incentivizes the …

Multimodal Mental Health Analysis In Social Media, Amir Hossein Yazdavar, Mohammad Saeid Mahdavinejad, Goonmeet Bajaj, William Romine, Amit P. Sheth, Amir Hassan Monadjemi, Krishnaprasad Thirunarayan, John M. Meddar, Annie Myers, Jyotishman Pathak, Pascal Hitzler

Publications

Depression is a major public health concern in the U.S. and globally. While successful early identification and treatment can lead to many positive health and behavioral outcomes, depression, remains undiagnosed, untreated or undertreated due to several reasons, including denial of the illness as well as cultural and social stigma. With the ubiquity of social media platforms, millions of people are now sharing their online persona by expressing their thoughts, moods, emotions, and even their daily struggles with mental health on social media. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of depressive …

Analyzing And Learning The Language For Different Types Of Harassment, Mohammadreza Rezvan, Saeedeh Shekarpour, Faisal Alshargi, Krishnaprasad Thirunarayan, Valerie L. Shalin, Amit P. Sheth

Publications

THIS ARTICLE USES WORDS OR LANGUAGE THAT IS CONSIDERED PROFANE, VULGAR, OR OFFENSIVE BY SOME READERS. The presence of a significant amount of harassment in user-generated content and its negative impact calls for robust automatic detection approaches. This requires the identification of different types of harassment. Earlier work has classified harassing language in terms of hurtfulness, abusiveness, sentiment, and profanity. However, to identify and understand harassment more accurately, it is essential to determine the contextual type that captures the interrelated conditions in which harassing language occurs. In this paper we introduce the notion of contextual type in harassment by distinguishing …

Finetuning Pre-Trained Language Models For Sentiment Classification Of Covid19 Tweets, Arjun Dussa

Dissertations

It is a common practice in today’s world for the public to use different micro-blogging and social networking platforms, predominantly Twitter, to share opinions, ideas, news, and information about many things in life. Twitter is also becoming a popular channel for information sharing during pandemic outbreaks and disaster events. The world has been suffering from economic crises ever since COVID-19 cases started to increase rapidly since January 2020. The virus has killed more than 800 thousand people ever since the discovery as per the statistics from Worldometer [1] which is the authorized tracking website. So many researchers around the globe …

Twitter And Disasters: A Social Resilience Fingerprint, Benjamin A. Rachunok, Jackson B. Bennett, Roshanak Nateghi

Purdue University Libraries Open Access Publishing Fund

Understanding the resilience of a community facing a crisis event is critical to improving its adaptive capacity. Community resilience has been conceptualized as a function of the resilience of components of a community such as ecological, infrastructure, economic, and social systems, etc. In this paper, we introduce the concept of a “resilience fingerprint” and propose a multi-dimensional method for analyzing components of community resilience by leveraging existing definitions of community resilience with data from the social network Twitter. Twitter data from 14 events are analyzed and their resulting resilience fingerprints computed. We compare the fingerprints between events and show that …

Enhancing Partially Labelled Data: Self Learning And Word Vectors In Natural Language Processing, Eamon Mcentee

Dissertations

There has been an explosion in unstructured text data in recent years with services like Twitter, Facebook and WhatsApp helping drive this growth. Many of these companies are facing pressure to monitor the content on their platforms and as such Natural Language Processing (NLP) techniques are more important than ever. There are many applications of NLP ranging from spam filtering, sentiment analysis of social media, automatic text summarisation and document classification.

Is There A Correlation Between Wikidata Revisions And Trending Hashtags On Twitter?, Paula Dooley [Thesis]

Dissertations

Twitter is a microblogging application used by its members to interact and stay socially connected by sharing instant messages called tweets that are up to 280 characters long. Within these tweets, users can add hashtags to relate the message to a topic that is shared among users. Wikidata is a central knowledge base of information relying on its members and machines bots to keeping its content up to date. The data is stored in a highly structured format with the added SPARQL protocol and RDF Query Language (SPARQL) endpoint to allow users to query its knowledge base.

Identifying Key Topics Bearing Negative Sentiment On Twitter: Insights Concerning The 2015-2016 Zika Epidemic, Ravali Mamidi, Michele Miller, Tanvi Banerjee, William Romine, Amit Sheth

Publications

Background To understand the public sentiment regarding the Zika virus, social media can be leveraged to understand how positive, negative, and neutral sentiments are expressed in society. Specifically, understanding the characteristics of negative sentiment could help inform federal disease control agencies’ efforts to disseminate relevant information to the public about Zika-related issues.

Objective The purpose of this study was to analyze the public sentiment concerning Zika using posts on Twitter and determine the qualitative characteristics of positive, negative, and neutral sentiments expressed.

Methods Machine learning techniques and algorithms were used to analyze the sentiment of tweets concerning Zika. A supervised …

The Global Disinformation Order: 2019 Global Inventory Of Organised Social Media Manipulation, Samantha Bradshaw, Philip N. Howard

Copyright, Fair Use, Scholarly Communication, etc.

Executive Summary

Over the past three years, we have monitored the global organization of social media manipulation by governments and political parties. Our 2019 report analyses the trends of computational propaganda and the evolving tools, capacities, strategies, and resources.

1. Evidence of organized social media manipulation campaigns which have taken place in 70 countries, up from 48 countries in 2018 and 28 countries in 2017. In each country, there is at least one political party or government agency using social media to shape public attitudes domestically.

2.Social media has become co-opted by many authoritarian regimes. In 26 countries, computational propaganda …

Exploring Offline Friendships On The Social Information Network: Network Characteristics, Information Diffusion, And Tie Strength, Felicia Natali

Dissertations and Theses Collection (Open Access)

The rapid increase in online social networking services over the last decade has pre- sented an unprecedented opportunity to observe users’ behaviour both on a societal and individual level. The insight gained from analysing such data can help foster a deeper understanding of social media users and the flow of information, while also offering valuable business applications. User relationships are among the most studied aspects of online behaviour. These relationships are not homogeneous. Past research has shown that people use social networks to both socialize and source in- formation. Hence, different types of links – used to socialize, gain information, …

Sentiment Analysis Of Twitter Data, Evan L. Munson

Theses and Dissertations

The rapid expansion and acceptance of social media has opened doors into users’ opinions and perceptions that were never as accessible as they are with today's prevalence of mobile technology. Harvested data, analyzed for opinions and sentiment can provide powerful insight into a population. This research utilizes Twitter data due to its widespread global use, in order to examine the sentiment associated with tweets. An approach utilizing Twitter #hashtags and Latent Dirichlet Allocation topic modeling were utilized to differentiate between tweet topics. A lexicographical dictionary was then utilized to classify sentiment. This method provides a framework for an analyst to …

A Twitter-Based Study For Understanding Public Reaction On Zika Virus, Roopteja Muppalla

Browse all Theses and Dissertations

In recent times, social media platforms like Twitter have become more popular and people have become more interactive and responsive than before. People often react to every news in real-time and within no-time, the information spreads rapidly. Even with viral diseases like Zika, people tend to share their opinions and concerns on social media. This can be leveraged by the health officials to track the disease in real-time thereby reducing the time lag due to traditional surveys. A faster and accurate detection of the disease can allow health officials to understand people's opinion of the disease and take necessary precautions …

A Framework To Understand Emoji Meaning: Similarity And Sense Disambiguation Of Emoji Using Emojinet, Sanjaya Wijeratne

Browse all Theses and Dissertations

Pictographs, commonly referred to as `emoji’, have become a popular way to enhance electronic communications. They are an important component of the language used in social media. With their introduction in the late 1990’s, emoji have been widely used to enhance the sentiment, emotion, and sarcasm expressed in social media messages. They are equally popular across many social media sites including Facebook, Instagram, and Twitter. In 2015, Instagram reported that nearly half of the photo comments posted on Instagram contain emoji, and in the same year, Twitter reported that the `face with tears of joy’ emoji has been tweeted 6.6 …

Developing A Recurrent Neural Network With High Accuracy For Binary Sentiment Analysis, Kevin Cunanan

CMC Senior Theses

Sentiment analysis has taken on various machine learning approaches in order to optimize accuracy, precision, and recall. However, Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) account for the context of a sentence by using previous predictions as additional input for future sentence predictions. Our approach focused on developing an LSTM RNN that could perform binary sentiment analysis for positively and negatively labeled sentences. In collaboration with Mariam Salloum, I developed a collection of programs to classify individual sentences as either positive or negative. This paper additionally looks into machine learning, neural networks, data preprocessing, implementation, and resulting comparisons.

Content-Based Clustering And Visualization Of Social Media Text Messages, Sydney A. Barnard

Browse all Theses and Dissertations

Although Twitter has been around for more than ten years, crisis management agencies and first response personnel are not able to fully use the information this type of data provides during a crisis or natural disaster. This thesis addresses clustering and visualizing social media data by textual similarity, rather than by only time and location, as a tool for first responders. This thesis presents a tool that automatically clusters geotagged text data based on their content and displays the clusters and their locations on the map. It allows at-a-glance information to be displayed throughout the evolution of a crisis. For …

A Semantically Enhanced Approach To Identify Depression-Indicative Symptoms Using Twitter Data, Ankita Saxena

Browse all Theses and Dissertations

According to the World Health Organization, more than 300 million people suffer from Major Depressive Disorder (MDD) worldwide. PHQ-9 is used to screen and diagnose MDD clinically and identify its severity. With the unprecedented growth and enthusiastic acceptance of social media such as Twitter, a large number of people have come to share their feelings and emotions on it openly. Each tweet can indicate a user's opinion, thought or feeling. A tweet can also indicate multiple symptoms related to PHQ-9. Identifying PHQ-9 symptoms indicated by a tweet can provide crucial information about a user regarding his/her depression diagnosis. The current …

An Analysis Of Rumor And Counter-Rumor Messages In Social Media, Dion Hoe-Lian Goh, Alton Y. K. Chua, Hanyu Shi, Wenju Wei, Haiyan Wang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Social media platforms are one of the fastest ways to disseminate information but they have also been used as a means to spread rumors. If left unchecked, rumors have serious consequences. Counter-rumors, messages used to refute rumors, are an important means of rumor curtailment. The objective of this paper is to examine the types of rumor and counter-rumor messages generated in Twitter in response to the falsely reported death of a politician, Lee Kuan Yew, who was Singapore’s first Prime Minister. Our content analysis of 4321Twitter tweets about Lee’s death revealed six categories of rumor messages, four categories ofcounter-rumor messages …

Analysis Of Central Banks Platforms On Social Networks, Goran Bjelobaba, Ana Savic, Hana Stefanovic

UBT International Conference

The paper describes the advantages of using technical and technological achievements through the social networks and their implementation in Central Banks communication. A study of some Central Banks on their use of social media and some of the most popular social networks are given, showing that social media is becoming an important medium of communication for Central Banks around the world. The study shows that a majority of Central Banks use Twitter to send alerts for information already disseminated through the website, even as they are not very active in responding to public tweets, and also shows an increasing number …

“How Short Is A Piece Of String?”: An Investigation Into The Impact Of Text Length On Short-Text Classification Accuracy, Austin Mccartney

Dissertations

The recent increase in the widespread use of short messages, for example micro-blogs or SMS communications, has created an opportunity to harvest a vast amount of information through machine-based classification. However, traditional classification methods have failed to produce accuracies comparable to those obtained from similar classification of longer texts. Several approaches have been employed to extend traditional methods to overcome this problem, including the enhancement of the original texts through the construction of associations with external data enrichment sources, ranging from thesauri and semantic nets such as Wordnet, to pre-built online taxonomies such as Wikipedia. Other avenues of investigation have …

The Evaluation Of Ensemble Sentiment Classification Approach On Airline Services Using Twitter, Zechen Wang

Dissertations

In the field of sentiment classification, much research has been done on reviews of topics such as movies, software and books. Little research has been done in the airline service domain. In the airline industry, the use of social media as a customer service tool has become a growing phenomenon. The research conducted by Wan and Gao (2015) has proposed an ensemble classification approach for airline service sentiment classification using Twitter data. In accordance, the objective of improving the performance of ensemble classification approach is the primary consideration. This research proposed new hybrid classification approach that uses the state-of-art approach …

Harassment Detection On Twitter Using Conversations, Venkatesh Edupuganti

Browse all Theses and Dissertations

Social media has brought people closer than ever before, but the use of social media has also brought with it a risk of online harassment. Such harassment can have a serious impact on a person such as causing low self-esteem and depression. The past research on detecting harassment on social media is primarily based on the content of messages exchanged on social media. The lack of context when relying on a single social media post can result in a high degree of false alarms. In this study, I focus on the reliable detection of harassment on Twitter by better understanding …

A Multilingual Semi-Supervised Approach In Deriving Singlish Sentic Patterns For Polarity Detection, Siaw Ling Lo, Erik Cambria, Raymond Chiong, David Cornforth

Research Collection School Of Computing and Information Systems

Due to the huge volume and linguistic variation of data shared online, accurate detection of the sentiment of a message (polarity detection) can no longer rely on human assessors or through simple lexicon keyword matching. This paper presents a semi-supervised approach in constructing essential toolkits for analysing the polarity of a localised scarce-resource language, Singlish (Singaporean English). Corpus-based bootstrapping using a multilingual, multifaceted lexicon was applied to construct an annotated testing dataset, while unsupervised methods such as lexicon polarity detection, frequent item extraction through association rules and latent semantic analysis were used to identify the polarity of Singlish n-grams before …