Sentiment Analysis of Twitter Data for Predicting Stock Market Movements

Predicting stock market movements is a well-known problem of interest. Now-a-days social media is perfectly representing the public sentiment and opinion about current events. Especially, twitter has attracted a lot of attention from researchers for studying the public sentiments. Stock market prediction on the basis of public sentiments expressed on twitter has been an intriguing field of research. Previous studies have concluded that the aggregate public mood collected from twitter may well be correlated with Dow Jones Industrial Average Index (DJIA). The thesis of this work is to observe how well the changes in stock prices of a company, the rises and falls, are correlated with the public opinions being expressed in tweets about that company. Understanding author's opinion from a piece of text is the objective of sentiment analysis. The present paper have employed two different textual representations, Word2vec and N-gram, for analyzing the public sentiments in tweets. In this paper, we have applied sentiment analysis and supervised machine learning principles to the tweets extracted from twitter and analyze the correlation between stock market movements of a company and sentiments in tweets. In an elaborate way, positive news and tweets in social media about a company would definitely encourage people to invest in the stocks of that company and as a result the stock price of that company would increase. At the end of the paper, it is shown that a strong correlation exists between the rise and falls in stock prices with the public sentiments in tweets.Comment: 6 pages 4 figures Conference Pape

Surveilling COVID-19 Emotional Contagion on Twitter by Sentiment Analysis

BACKGROUND: The fight against the COVID-19 pandemic seems to encompass a social media debate, possibly resulting in emotional contagion and the need for novel surveillance approaches. In the current study, we aimed to examine the flow and content of tweets, exploring the role of COVID-19 key events on the popular Twitter platform. METHODS: Using representative freely available data, we performed a focused, social media-based analysis to capture COVID-19 discussions on Twitter, considering sentiment and longitudinal trends between January 19 and March 3, 2020. Different populations of users were considered. Core discussions were explored measuring tweets’ sentiment, by both computing a polarity compound score with 95% Confidence Interval and using a transformer-based model, pretrained on a large corpus of COVID-19-related Tweets. Context-dependent meaning and emotion-specific features were considered. RESULTS: We gathered 3,308,476 tweets written in English. Since the first World Health Organization report (January 21), negative sentiment proportion of tweets gradually increased as expected, with amplifications following key events. Sentiment scores were increasingly negative among most active users. Tweets content and flow revealed an ongoing scenario in which the global emergency seems difficult to be emotionally managed, as shown by sentiment trajectories. CONCLUSIONS: Integrating social media like Twitter as essential surveillance tools in the management of the pandemic and its waves might actually represent a novel preventive approach to hinder emotional contagion, disseminating reliable information and nurturing trust. There is the need to monitor and sustain healthy behaviors as well as community supports also via social media-based preventive interventions

Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks

Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Motivated by the needs in leveraging large scale yet noisy training data to solve the extremely challenging problem of image sentiment analysis, we employ Convolutional Neural Networks (CNN). We first design a suitable CNN architecture for image sentiment analysis. We obtain half a million training samples by using a baseline sentiment algorithm to label Flickr images. To make use of such noisy machine labeled data, we employ a progressive strategy to fine-tune the deep network. Furthermore, we improve the performance on Twitter images by inducing domain transfer with a small number of manually labeled Twitter images. We have conducted extensive experiments on manually labeled Twitter images. The results show that the proposed CNN can achieve better performance in image sentiment analysis than competing algorithms.Comment: 9 pages, 5 figures, AAAI 201

What Sentiment and Fun Facts We Learnt Before FIFA World Cup Qatar 2022 Using Twitter and AI

Twitter is a social media platform bridging most countries and allows real-time news discovery. Since the tweets on Twitter are usually short and express public feelings, thus provide a source for opinion mining and sentiment analysis for global events. This paper proposed an effective solution, in providing a sentiment on tweets related to the FIFA World Cup. At least 130k tweets, as the first in the community, are collected and implemented as a dataset to evaluate the performance of the proposed machine learning solution. These tweets are collected with the related hashtags and keywords of the Qatar World Cup 2022. The Vader algorithm is used in this paper for sentiment analysis. Through the machine learning method and collected Twitter tweets, we discovered the sentiments and fun facts of several aspects important to the period before the World Cup. The result shows people are positive to the opening of the World Cup

Is That Twitter Hashtag Worth Reading

Online social media such as Twitter, Facebook, Wikis and Linkedin have made a great impact on the way we consume information in our day to day life. Now it has become increasingly important that we come across appropriate content from the social media to avoid information explosion. In case of Twitter, popular information can be tracked using hashtags. Studying the characteristics of tweets containing hashtags becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, and sentiment analysis among others. In this paper, we have analyzed Twitter data based on trending hashtags, which is widely used nowadays. We have used event based hashtags to know users' thoughts on those events and to decide whether the rest of the users might find it interesting or not. We have used topic modeling, which reveals the hidden thematic structure of the documents (tweets in this case) in addition to sentiment analysis in exploring and summarizing the content of the documents. A technique to find the interestingness of event based twitter hashtag and the associated sentiment has been proposed. The proposed technique helps twitter follower to read, relevant and interesting hashtag.Comment: 10 pages, 6 figures, Presented at the Third International Symposium on Women in Computing and Informatics (WCI-2015

The impact of the 2015 refugee crisis on the international place brand of Hungary

This quantitative study examines how the 2015 European refugee crisis events affected the international place brand of Hungary on Twitter in both short and long terms. The study supports the application of quantitative methods and dictionary-based sentiment analysis of tweets to the discipline of place branding. There is a significant increase in the amount of negative sentiment in tweets during the crisis (July-October, 2015) due to a high number of tweets about the refugee crisis. However, this effect has not persisted after November 2015. To conduct the sentiment analysis, we apply the lexicon-based polarity dictionary SentiStrength; to divide tweets into specific topics, we use Latent Dirichlet Allocation (Blei et al, 2003). The tweets that are likely published by media organizations are excluded from the analysis. There is no significant increase in the amount of negative sentiment in tweets after the crisis, which suggests no persistent effect of the crisis on the place brand of Hungary in the long term because that negative sentiment about the crisis comes only from the tweets about the refugee crisis. The contribution of this study is the establishment of a research framework for the social media analysis of place brands in a crisis as well as in forming a solid basis for the application of this framework to studying other place brands in a crisis

Exploring Sentiment Analysis on Twitter: Investigating Public Opinion on Migration in Brazil from 2015 to 2020

openTechnology has reshaped societal interaction and the expression of opinions. Migration is a prominent trend, and analysing social media discussions provides insights into societal perspectives. This thesis explores how events between 2015 and 2020 impacted Brazilian sentiment on Twitter about migrants and refugees. Its aim was to uncover the influence of key sociopolitical events on public sentiment, clarifying how these echoed in the digital realm. Four key objectives guided this research: (a) understanding public opinions on migrants and refugees, (b) investigating how events influenced Twitter sentiment, (c) identifying terms used in migration-related tweets, and (d) tracking sentiment shifts, especially concerning changes in government. Sentiment analysis using VADER (Valence Aware Dictionary and sEntiment Reasoner) was employed to analyse tweet data. The use of computational methods in social sciences is gaining traction, yet no analysis has been conducted before to understand the sentiments of the Brazilian population regarding migration. The analysis underscored Twitter's role in reflecting and shaping public discourse, offering insights into how major events influenced discussions on migration. In conclusion, this study illuminated the landscape of Brazilian sentiment on migration, emphasizing the significance of innovative social media analysis methodologies for policymaking and societal inclusivity in the digital age

The power of twitter on predicting box office revenues

Over the last few years there has been an extraordinary surge of social networking and microblogging services. Twitter is a social network that focuses on social and news media. The Twitter data stream allows access to tweets, timestamps and locations of users. This enables us to capture the trends and patterns of rapidly evolving worldwide events. We use the Twitter data stream for the prediction of consumer preferences in the movie industry and estimate how successful the movie will be in the first and second weekends since its release date. The study provides evidence to suggest that frequencies of contemporaneous tweets and a consensus measure of public sentiment are useful for predicting box-office revenues, implying that any publicity is good publicity in word-of-mouth (WOM) and online viral marketing. Sentiment analysis based on tweets suggests that more extreme sentiment has more impact, and that the more negative the tweets about a movie are, the higher its revenue will be, in contrast with the classic theory of diffusion in news media