Emotions In Social Media Research Articles

Beyond negative and positive: Exploring the effects of emotions in social media during the stock market crash

Recent studies indicate that the stock market is influenced by emotion in social media (ESM) which are embodied in user-generated content. However, the relationship between ESM and the stock market in the event of the market crash has not been fully explored. This study thus explores the effects of ESM on the stock market during the market crash by empirically validating the proposed cognition-based framework of “Emotion-Cognition-Market”. A three-component model is constructed for the framework, which uses sentiment analysis to calculate emotions from two-dimension of valence and arousal, uses Hidden Markov Model (HMM) for market cognition mining, and uses ordered logistic regression for relationship establishment between ESM and market cognition. More than 280,000 Weibo of 34 listed companies during the market crash (the second half-year of 2015 in China) are used. It is confirmed that the impact process of ESM on the stock market is actually the result of constantly changing market cognition influenced by ESM. This study goes beyond the commonly used positive and negative (polar) emotions or sub-categorical emotions, discovering the salient effects of arousal dimension in the market crash, and also quantify the effects. The results show that the increase of one unit high arousal ESM significantly increases the probability of the cognition of a crash state by 9.99 and 17.41% during the in-crash and post-crash period, of which “Fear” is the main risk factor. In addition, positive valence ESM is the driving force of restoring the stability of the market cognition only in the later stage of the market crash. This paper calls on market participants to pay attention to high arousal emotions in emergency situations in advance.

Implementation of Recurrent Network for Emotion Recognition of Twitter Data

A new generation of emoticons, called emojis, is being largely used for both mobile and social media communications. Emojis are considered a graphic expression of emotions, and users have been widely used to express their emotions in social media. Emojis are graphic unicode symbols used to express perceptions, views, and ideas as a shorthand. Unlike the small number of well-known emoticons carrying clear emotional content, hundreds of emojis are being used in different social networks. The task of emoji emotion recognition is to predict the original emoji in a tweet. Recurrent neural network is used for building emoji emotion recognition system. Glove is a word-embedding method used for obtaining vector representation of words and are used for training the recurrent neural network. This is achieved by mapping words into a meaningful space where the distance between words is related to semantic similarity. Based on the word embedding in the Twitter dataset, recurrent neural network builds the model and finally predicts the emoji associated with the tweets with an accuracy of 83%.

Impact of the COVID-19 pandemic on the expression of emotions in social media

In the age of social media, every second thousands of messages are exchanged. Analyzing those unstructured data to find out specific emotions is a challenging task. Analysis of emotions involves evaluation and classification of text into emotion classes such as Happy, Sad, Anger, Disgust, Fear, Surprise, as defined by emotion dimensional models which are described in the theory of psychology (www 1; Russell, 2005). The main goal of this paper is to cover the COVID-19 pandemic situation in India and its impact on human emotions. As people very often express their state of the mind through social media, analyzing and tracking their emotions can be very effective for government and local authorities to take required measures. We have analyzed different machine learning classification models, such as Naïve Bayes, Support Vector Machine, Random Forest Classifier, Decision Tree and Logistic Regression with 10-fold cross validation to find out top ML models for emotion classification. After tuning the Hyperparameter, we got Logistic regression as the best suited model with accuracy 77% with the given datasets. We worked on algorithm based supervised ML technique to get the expected result. Although multiple studies were conducted earlier along the same lines, none of them performed comparative study among different ML techniques or hyperparameter tuning to optimize the results. Besides, this study has been done on the dataset of the most recent COVID-19 pandemic situation, which is itself unique. We captured Twitter data for a duration of 45 days with hashtag #COVID19India OR #COVID19 and analyzed the data using Logistic Regression to find out how the emotion changed over time based on certain social factors. Keywords: classification, COVID-19, emotion, emotion analysis, Naïve Bayes, Pandemic, Random Forest, SVM.

Emotions in Social Media: An Analysis of Tweet Responses to MH370 Search Suspension Announcement

Considered one of the deadliest incidents in the history of aviation crises and labelled a “continuing mystery,” the ongoing search for the missing Malaysia Airlines Flight 370 offers no closure. With endless media attention, and negative reactions of stakeholders to every decision made by the airline, this study investigates the types of emotions found in social media posted by publics to the MH370 search suspension announcement. It content analyzed 5,062 real-time tweet messages guided by the revised integrated crisis mapping model. Our findings indicated that, in addition to the four original emotions posited, there was a fifth emotion because of the long-drawn crisis and only two dominant emotions were similar to the model. A redrawn version to better encapsulate all the emotions is offered for one quadrant in the model. Implications for both crisis communication scholarship and the importance of social listening for organizations are discussed.

Affected and connected: feminist and psychological perspectives on emotion in social media

ABSTRACT This article works across psychology and feminist media studies to offer an empirically grounded investigation of affective expression in social media. To work empirically with understandings of affect, and to examine affective engagements via social media, we explore Instagram fans’ responses to the Norwegian TV series Skam (translation: “shame”). We draw on feminist theoretical perspectives on affectivity and emotion, and critical psychological perspectives on well-being. We ask: how are affect and emotion woven through contributors’ expressions, what do contributors write about their sense of belonging, and what is hoped for through affective engagement? By analyzing contributors’ writing about their experiences of how Skam has affected their lives, this study illustrates how contributors to social media produce the idea of life as radically open to transformation. This analysis demonstrates how passionately imagined connections create new opportunities for becoming and belonging.

Momentum, Mean-Reversion, and Social Media: Evidence from StockTwits and Twitter

In this article, the authors analyze the relation between stock market liquidity and real-time measures of sentiment obtained from the social-media platforms StockTwits and Twitter. The authors find that extreme sentiment corresponds to higher demand for and lower supply of liquidity, with negative sentiment having a much larger effect on demand and supply than positive sentiment. Their intraday event study shows that booms and panics end when bullish and bearish sentiment reach extreme levels, respectively. After extreme sentiment, prices become more mean-reverting and spreads narrow. To quantify the magnitudes of these effects, the authors conduct a historical simulation of a market-neutral mean-reversion strategy that uses social-media information to determine its portfolio allocations. These results suggest that the demand for and supply of liquidity are influenced by investor sentiment and that market makers who can keep their transaction costs to a minimum are able to profit by using extreme bullish and bearish emotions in social media as a real-time barometer for the end of momentum and a return to mean reversion. <b>TOPICS:</b>Security analysis and valuation, statistical methods

Social Media Emotion in Politics: An Indonesian Case Study of Political Environment on Facebook

The objective of the study was to investigate social media users’ attitude toward the current political environment displayed on their social media platform. The study is interesting due to the fact that nowadays even simple chats on social media can go awry when politics comes across as a topic. The study was conducted during and after the recent 2017 political election in Jakarta, Indonesia. Survey study was conducted in collecting the data with the total of 704 social media users participated in this study. The results indicate that social media is not a friendly zone when it comes to political debates. Few social media users got a thrill out of the opportunities for political debates on social media. However, many of the respondent’s express frustration and weariness over the content and tone of political environment on their social media timeline.

Momentum, Mean-Reversion and Social Media: Evidence from StockTwits and Twitter

We analyze the relation between stock market liquidity and real-time measures of sentiment obtained from the social-media platforms StockTwits and Twitter. Linear regression analysis shows that extreme sentiment corresponds to higher demand and lower supply of liquidity, with negative sentiment having a much larger effect on demand and supply than positive sentiment. An intraday event study shows that booms and panics end when bullish and bearish sentiment reach extreme levels, respectively. After extreme sentiment, prices become more mean-reverting and spreads narrow. To quantify the magnitudes of these effects, we conduct a historical simulation of a market-neutral mean-reversion strategy that uses social-media information to determine its portfolio allocations. Our results suggest that the demand and supply of liquidity are influenced by investor sentiment, and that market makers who can keep their transaction costs to a minimum are able to profit by using extreme bullish and bearish emotions in social media as a real-time barometer for the end of momentum and a return to mean reversion.

#Humanity washed ashore. Visual metaphors and emotions in social media

#Humanity washed ashore. Visual metaphors and emotions in social media

Mapping Urban Emotions of Seoul: Using Social Media Emotion Information to Explore the Urban Landscape of the Cit

Mapping Urban Emotions of Seoul: Using Social Media Emotion Information to Explore the Urban Landscape of the Cit

News and social media emotions in the commodity market

PurposeEmotion plays a significant role in both institutional and individual investors’ decision-making process. Emotions affect the perception of risk and the assessment of monetary value. However, there is a lack of empirical evidence available that addresses how investors’ emotions affect commodity market returns. The purpose of this paper is to investigate whether media-based emotions can be used to predict future commodity returns.Design/methodology/approachThe authors examine the short-term predictive power of media-based emotion indices on the following five days’ commodity returns. The research adopts a proprietary data set of commodity-specific market emotions, which is computed based on a comprehensive textual analysis of sources from newswires, internet news sources and social media. Time series econometrics models (threshold generalized autoregressive conditional heteroskedasticity and vector autoregressive) are employed to analyze 14 years (January 1998-December 2011) of daily observations of the CRB commodity market index, crude oil and gold returns, and the market-level sentiments and emotions (optimism, fear and joy).FindingsThe empirical results suggest that the commodity-specific emotions (optimism, fear and joy) have significant influence on individual commodity returns, but not on commodity market index returns. Additionally, the research findings support the short-term predictability of the commodity-specific emotions on the following five days’ individual commodity returns. Compared to the previous studies of news sentiment on commodity returns (Borovkova, 2011; Borovkova and Mahakena, 2015; Smales, 2014), this research provides further evidence of the effects of news and social media-based emotions (optimism, fear and joy) in the commodity market. Additionally, this work proposes that market emotion incorporates both a sentimental effect and appraisal effect on commodity returns. Empirical results are shown to support both the sentimental effect and appraisal effect when market sentiment is controlled in crude oil and gold spot markets.Originality/valueThis paper adopts the valence-arousal approach and cognitive appraisal approach to explain financial anomalies caused by investors’ emotions. Additionally, this is the first paper to explore the predictive power of investors’ emotions (optimism, fear and joy) on commodity returns.

Sentiment and emotion classification over noisy labels

With the rapid development of social media, online users are allowed to share their opinions conveniently. However, the ground truth for sentiments and emotions in social media is often constructed through surveys, hashtags or emoticons, where the labels may contain many errors. There are also amateurs and malicious users expressing offensive opinions or spreading fraudulent reviews, which has been identified as a growing threat to the trustworthiness of online comments. Thus, it is valuable for us to reconcile this noise in the ground truth when training sentiment and emotion classifiers. In this paper, we propose a hidden de-noising classification model (HDCM) that does not need any outsourcing systems or lexicons to estimate the actual sentimental or emotional category of each instance from corpora with noisy labels. The simplicity of assigning the category to a document by users under any contexts, and the authority of a user in assigning categories to documents with various domains are modeled as the unobserved hidden constraints in HDCM. Extensive evaluations using datasets with different scales of noisy labels validate the effectiveness of the proposed model for both sentiment and emotion classification tasks.

Social Media – An Arena for Venting Negative Emotions

Social media is seen as transforming into a global multiplier through which emotional experiences are shared and strengthened. The essential factor in the ongoing transformation is that, although emotions are felt on an individual level, in social media, they can simultaneously be shared with and by others. Many studies have shown that social media is an arena for sharing information that reflects negative emotions. The theme of the paper is important as nowadays people have access to online discussions, blogs and even websites devoted entirely to sharing negative emotional experiences. After reviewing the literature, the paper explores and discusses the implications of negative emotions shared in social media. The main contribution of the paper is the anatomy of the diffusion of collective negative emotion in social media. In addition, the paper discusses the positive consequences of negative emotions from an organisation’s perspective.