Scalable System for Opinion Mining on Twitter Data. Dynamic Visualization for Data Related to Refugees’ Crisis and to Terrorist Attacks

Social networks such as Twitter or Facebook grew rapidly in popularity, and users use them to share opinions about topics of interest, to be part of the community or to post messages that are available everywhere. This paper presents a system created in order to process streamed data taken from Twitter and classify it into positive, negative or neutral. The results of these processing’s can be visualized in a suggestive manner on Google Maps, users can select the language of the tweets, can group tweets that present the same news and can even display a dynamic evolution of the news in terms of its appearance. With all this amount of information it is very opportune to do some data analysis to detect different types of events (and their locations) that happen worldwide, especially at the time when this data represents information related to refugee crisis or signals terrorist attacks

GreekPolitics: Sentiment Analysis on Greek Politically Charged Tweets

The rapid growth of on-line social media platforms has rendered opinion mining/sentiment analysis a critical area of research. This paper focuses on analyzing Twitter posts (tweets), written in the Greek language and politically charged in content. This is a rather underexplored topic, due to the inadequacy of publicly available annotated datasets. Thus, we present and release GreekPolitics: a dataset of Greek tweets with politically charged content, annotated for four different sentiments: polarity, figurativeness, aggressiveness and bias. GreekPolitics has been evaluated comprehensively using state-of-the-art Deep Neural Networks (DNNs) and data augmentation methods. This paper details the dataset, the evaluation process and the experimental results

Sentiment Analysis on Twitter Data and Social Trends: The Case of Greek General Elections

Η ανάλυση συναισθήματος και εξόρυξη γνώμης (Sentiment Analysis-Opinion Mining) είναι η διαδικασία χρήσης επεξεργασίας φυσικής γλώσσας και διαφόρων τεχνικών (μηχανική μάθηση, λεξικά) για τον εντοπισμό και την εξαγωγή υποκειμενικών πληροφοριών από δεδομένα κειμένου. Χρησιμοποιείται συνήθως για τον προσδιορισμό του συνολικού συναισθήματος ενός κειμένου, όπως αν είναι θετικό, αρνητικό ή ουδέτερο. Σκοπός της παρούσας Διπλωματικής Εργασίας είναι η ανάλυση του συναισθήματος σε δεδομένα του Twitter. Πιο συγκεκριμένα, εφαρμόστηκε μια προσέγγιση βασισμένη σε λεξικό για την ανάλυση του συναισθήματος σε κείμενο tweet που σχετίζεται με τις Βουλευτικές Εκλογές του 2019 στην Ελλάδα. Τα tweets είναι στην ελληνική γλώσσα και ταξινομούνται ως θετικά, αρνητικά και ουδέτερα με βάση το συνολικό συναίσθημα που εκφράζουν. Μέσω της ανάλυσης συναισθήματος στα σύνολα δεδομένων με τη χρήση της γλώσσας προγραμματισμού Python, εξάγουμε συμπεράσματα σχετικά με τις κοινωνικές τάσεις που αναπτύσσονται στο προεκλογικό twitter σε σχέση με τα έξι (6) πολιτικά κόμματα που εξέλεξαν βουλευτές σε αυτές τις εκλογές. Τα αποτελέσματα παρουσιάζονται με σαφείς οπτικοποιήσεις με τη χρήση του εργαλείου Tableau για πληρέστερη κατανόηση. Εκτός από την περιγραφή της υλοποίησης, παρουσιάζονται οι κυριότεροι περιορισμοί και οι προκλήσεις και δυσκολίες που προέκυψαν στην προσπάθεια επεξεργασίας της ελληνικής γλώσσας. Τέλος, επιχειρείται η να επισήμανση ορισμένων πτυχών της ανάλυσης συναισθήματος και εξόρυξης γνώμης που χρήζουν βελτίωσης, τόσο στη προτεινόμενη εφαρμογή που παρουσιάζεται εδώ όσο και σε άλλες υπάρχουσες.Sentiment analysis and Opinion Mining involve the process of using natural language processing and various techniques (machine learning, lexicons) to identify and extract subjective information from text data. Sentiment analysis and Opinion Mining are commonly used to determine the emotional tone of a piece of text, such as whether it is positive, negative, or neutral. The purpose of the present Thesis is to analyze sentiment in Twitter data. More specifically, a lexicon-based approach has been implemented to analyze sentiment in tweet texts related to the 2019 general elections in Greece. The tweets are in the Greek language and are classified as positive, negative, and neutral based on the overall sentiment they express. Sentiment analysis implemented on the datasets using the Python programming language allows insights and conclusions about the social trends that develop in pre-election twitter in relation to the six (6) political parties that elected Members of Parliament (MPs) in the 2019 elections. The results are presented with visualizations using the Tableau tool targeting to a clear and more complete understanding. In addition to the description of the implementation, the main challenges, limitations, and difficulties encountered in trying to process the Greek language are presented, along with aspects of the implementation that can be improved, as well as other existing issues in Sentiment analysis and Opinion Mining

Impact of Social Media on Dubai Stock Market using Sentiment Analysis

One of the main objectives of having securities stock markets is to ensure fair trading. Our analysis of this study will show how sentiment analysis and text mining techniques can help stock markets to sense wipes in the market participants\u27 behaviors and how the market community can benefit from it. The ability to detect potential insiders and investors\u27 mood would help the stock market to take necessary actions to protect the trading environment and enhance investors’ trust in the market. In this project, we will be building a pilot proof of concept utilizing sentiment analysis on Twitter, one of the most popular social media applications, and Dubai Financial Market, one of the most active stock markets in the United Arab Emirates (UAE), in the English language. The project can grow in sophistication and coverage in the future. In this project, I am using R as a primary development tool where a statistical and visual analysis will be carried out utilizing its rich open community libraries

Gender inequality on Twitter during the UK election of 2019

Social media platforms such as Twitter play an essential role in politics and social movements nowadays. The aim of this paper is to compare and contrast the language used on Twitter to refer to the candidates of the last UK general election of December 2019 in order to raise awareness of gender inequality in politics. The methodology followed is based on three aspects: (a) a quantitative analysis using Sketch Engine to extract the main collocates from the corpus; (b) a sentiment analysis of the compiled tweets by means of two lexicon classifications: BING (Hu & Liu, 2004) and NRC (Mohammad & Turney, 2013), which classifies words into eight basic emotions and two sentiments (positive and negative); and (c) a qualitative analysis employing a Critical Discourse Analysis approach (Fairclough, 2013) to examine verbal abuse towards women from a linguistics perspective