A bilingual approach for conducting Chinese and English social media sentiment analysis

Due to the advancement of technology and globalization, it has become much easier for people around the world to express their opinions through social media platforms. Harvesting opinions through sentiment analysis from people with different backgrounds and from different cultures via social media platforms can help modern organizations, including corporations and governments understand customers, make decisions, and develop strategies. However, multiple languages posted on many social media platforms make it difficult to perform a sentiment analysis with acceptable levels of accuracy and consistency. In this paper, we propose a bilingual approach to conducting sentiment analysis on both Chinese and English social media to obtain more objective and consistent opinions. Instead of processing English and Chinese comments separately, our approach treats review comments as a stream of text containing both Chinese and English words. That stream of text is then segmented by our segment model and trimmed by the stop word lists which include both Chinese and English words. The stem words are then processed into feature vectors and then applied with two exchangeable natural language models, SVM and N-Gram. Finally, we perform a case study, applying our proposed approach to analyzing movie reviews obtained from social media. Our experiment shows that our proposed approach has a high level of accuracy and is more effective than the existing learning-based approaches.     

Discovering public sentiment in social media for predicting stock movement of publicly listed companies

The popularity of many social media sites has prompted both academic and practical research on the possibility of mining social media data for the analysis of public sentiment. Studies have suggested that public emotions shown through Twitter could be well correlated with the Dow Jones Industrial Average. However, it remains unclear how public sentiment, as reflected on social media, can be used to predict stock price movement of a particular publicly-listed company. In this study, we attempt to fill this research void by proposing a technique, called SMeDA-SA, to mine Twitter data for sentiment analysis and then predict the stock movement of specific listed companies. For the purpose of experimentation, we collected 200 million tweets that mentioned one or more of 30 companies that were listed in NASDAQ or the New York Stock Exchange. SMeDA-SA performs its task by first extracting ambiguous textual messages from these tweets to create a list of words that reflects public sentiment. SMeDA-SA then made use of a data mining algorithm to expand the word list by adding emotional phrases so as to better classify sentiments in the tweets. With SMeDA-SA, we discover that the stock movement of many companies can be predicted rather accurately with an average accuracy over 70%. This paper describes how SMeDA-SA can be used to mine social media date for sentiments. It also presents the key implications of our study.     

A framework for real-time semantic social media analysis

This paper presents a framework for collecting and analysing large volume social media content. The real-time analytics framework comprises semantic annotation, Linked Open Data, semantic search, and dynamic result aggregation components. In addition, exploratory search and sense-making are supported through information visualisation interfaces, such as co-occurrence matrices, term clouds, treemaps, and choropleths. There is also an interactive semantic search interface (Prospector), where users can save, refine, and analyse the results of semantic search queries over time. Practical use of the framework is exemplified through three case studies: a general scenario analysing tweets from UK politicians and the public’s response to them in the run up to the 2015 UK general election, an investigation of attitudes towards climate change expressed by these politicians and the public, via their engagement with environmental topics, and an analysis of public tweets leading up to the UK’s referendum on leaving the EU (Brexit) in 2016. The paper also presents a brief evaluation and discussion of some of the key text analysis components, which are specifically adapted to the domain and task, and demonstrate scalability and efficiency of our toolkit in the case studies.     

SAMAR: Subjectivity and sentiment analysis for Arabic social media

SAMAR is a system for subjectivity and sentiment analysis (SSA) for Arabic social media genres. Arabic is a morphologically rich language, which presents significant complexities for standard approaches to building SSA systems designed for the English language. Apart from the difficulties presented by the social media genres processing, the Arabic language inherently has a high number of variable word forms leading to data sparsity. In this context, we address the following 4 pertinent issues: how to best represent lexical information; whether standard features used for English are useful for Arabic; how to handle Arabic dialects; and, whether genre specific features have a measurable impact on performance. Our results show that using either lemma or lexeme information is helpful, as well as using the two part of speech tagsets (RTS and ERTS). However, the results show that we need individualized solutions for each genre and task, but that lemmatization and the ERTS POS tagset are present in a majority of the settings.     

HarVis: An integrated social media content analysis framework for YouTube platform

YouTube (owned by Google Inc.) is arguably among most popular social media platforms used by millions across the globe. It provides an ever-growing, unique and rich source of content which presents new opportunities and challenges for information discovery and analysis. It is pertinent to explore and understand a topic via YouTube content to discover interesting information about public opinions and sentiments. This paper presents an integrated framework to facilitate the acquisition, storage, management, processing, and visualization of relevant content with the objective to assist in such analysis. It not only collects a significant portion of content, relevant to a given topic, in short time but also offers tools for visual exploratory analysis such as; (i) temporal evolution, (ii) vocabulary network, (iii) authors relative popularity and influence (iv) categories and (v) user communities and influencers. The utility and effectiveness is demonstrated through content analysis of a famous YouTube entertainment topic, the “Gangnam Style”.     

Dissecting emotion and user influence in social media communities: An interaction modeling approach

Human emotion expressed in social media plays an increasingly important role in shaping policies and decisions. However, the process by which emotion produces influence in online social media networks is relatively unknown. Previous works focus largely on sentiment classification and polarity identification but do not adequately consider the way emotion affects user influence. This research developed a novel framework, a theory-based model, and a proof-of-concept system for dissecting emotion and user influence in social media networks. The system models emotion-triggered influence and facilitates analysis of emotion-influence causality in the context of U.S. border security (using 5,327,813 tweets posted by 1,303,477 users). Motivated by a theory of emotion spread, the model was integrated in an influence-computation method, called the interaction modeling (IM) approach, which was compared with a benchmark using a user centrality (UC) approach based on social positions. IM was found to have identified influential users who are more broadly related to U.S. cultural issues. Influential users tended to express intense emotions of fear, anger, disgust, and sadness. The emotion trust distinguishes influential users from others, whereas anger and fear contributed significantly to causing user influence. The research contributes to incorporating human emotion into the data-information-knowledge-wisdom model of knowledge management and to providing new information systems artifacts and new causality findings for emotion-influence analysis.     

SentiMI: Introducing point-wise mutual information with SentiWordNet to improve sentiment polarity detection

Supervised learning has attracted much attention in recent years. As a consequence, many of the state-of-the-art algorithms are domain dependent as they require a labeled training corpus to learn the domain features. This requires the availability of labeled corpora which is a cumbersome task in itself. However, for text sentiment detection SentiWordNet (SWN) may be used. It is a vocabulary where terms are arranged in synonym groups called synsets. This research makes use of SentiWordNet and treats it as the labeled corpus for training. A sentiment dictionary, SentiMI, builds upon the mutual information calculated from these terms. A complete framework is developed by using feature selection and extracting mutual information, from SentiMI, for the selected features. Training, testing and evaluation of the proposed framework are conducted on a large dataset of 50,000 movie reviews. A notable performance improvement of 7% in accuracy, 14% in specificity, and 8% in F-measure is achieved by the proposed framework as compared to the baseline SentiWordNet classifier. Comparison with the state-of-the-art classifiers is also performed on widely used Cornell Movie Review dataset which also proves the effectiveness of the proposed approach.     

Advanced social media sentiment analysis for short-term cryptocurrency price prediction

In recent years, the scrutiny of bitcoin and other cryptocurrencies as legal and regulated components of financial systems has been increasing. Bitcoin is currently one of the largest cryptocurrencies in terms of capital market share. Therefore, this study proposes that sentiment analysis can be used as a computational tool to predict the prices of bitcoin and other cryptocurrencies for different time intervals. A key characteristic of the cryptocurrency market is that the fluctuation of currency prices depends on people's perceptions and opinions, not institutional money regulation. Therefore, analysing the relationship between social media and web search is crucial for cryptocurrency price prediction. This study uses Twitter and Google Trends to forecast the short-term prices of the primary cryptocurrencies, as these social media platforms are used to influence purchasing decisions. The study adopts and interpolates a unique multimodel approach to analyse the impact of social media on cryptocurrency prices. Our results prove that people's psychological and behavioural attitudes have a significant impact on the highly speculative cryptocurrency prices.     

Beyond positive or negative: Qualitative sentiment analysis of social media reactions to unexpected stressful events

Sentiment analysis techniques are increasingly used to grasp reactions from social media users to unexpected and potentially stressful social events. This paper argues that, alongside assessments of the affective valence of social media content as negative or positive, there is a need for a deeper understanding of the context in which reactions are expressed and the specific functions that users' emotional states may reflect. To demonstrate this, we present a qualitative analysis of affective expressions on Twitter collected in Germany during the 2011 EHEC food contamination incident based on a coding scheme developed from Skinner et al.'s (2003) coping classification framework. Affective expressions of coping were found to be diverse not only in terms of valence but also in the adaptive functions they served: beyond the positive or negative tone, some people perceived the outbreak as a threat while others as a challenge to cope with. We discuss how this qualitative sentiment analysis can allow a better understanding of the way the overall situation is perceived – threat or challenge – and the resources that individuals experience having to cope with emerging demands.     

Recognizing CEO personality and its impact on business performance: Mining linguistic cues from social media

Upper echelons theory suggests that CEO personality will influence organizational performance. However, difficulty in measuring CEO personality restrains related research. We capture linguistic cues CEOs leaving on social media and recognize their personality by text mining. To our knowledge, it is the first study introducing social media text mining approaches into the research stream that empirically inquires and extends upper echelons theory. Then, we investigate the CEO personality’s impact on both operational and financial performance. Results show that CEO Extraversion, Emotional Stability, and Agreeableness improve Cost Efficiency and Profitability, while CEO Conscientiousness reduces them. CEO Openness to Experience negatively influences Profitability, and all facets of CEO personality improve Employee Productivity except for CEO Conscientiousness. The contribution of our research is multi-sided: (1). methodologically, we introduce a text mining approach to measure CEO personality; (2). theoretically, we provide empirical evidence for upper echelons theory; (3). practically, our results help companies evaluate CEO candidates from a personality perspective.     

Applying social learning analytics to message boards in online distance learning: A case study

Social learning analytics introduces tools and methods that help improving the learning process by providing useful information about the actors and their activity in the learning system. This study examines the relation between SNA parameters and student outcomes, between network parameters and global course performance, and it shows how visualizations of social learning analytics can help observing the visible and invisible interactions occurring in online distance education.The findings from our empirical study show that future research should further investigate whether there are conditions under which social network parameters are reliable predictors of academic performance, but also advises against relying exclusively in social network parameters for predictive purposes. The findings also show that data visualization is a useful tool for social learning analytics, and how it may provide additional information about actors and their behaviors for decision making in online distance learning.     

Crowdsourcing the perceived urban built environment via social media: The case of underutilized land

Crowdsourcing the public’s perceptions of the built environment in real time enables more responsive and agile infrastructure and land use planning. Social media has emerged to be an effective platform for citizens, engineers, and planners to communicate opinions and feelings transparently. However, a comprehensive terminological resource of the perceived built environment (BE) for consistent data collection and a specified analytical framework are still lacking, particularly for different underutilized land issues. To fill this knowledge gap, we demonstrate a BE-specific term construction and expansion method specifically for collecting Twitter data and propose a Geo-Topic-Sentiment analytical framework for retrieving and analyzing relevant tweets. We conduct a demonstrative study on un(der)utilized land-related BE terms across ten metropolitan statistical areas in the U.S. Findings reveal spatial variations in contents and sentiments about underutilized land environments, and more localized efforts may be required to address specific land use issues across different urban contexts. The research demonstrates Twitter as a useful platform in crowdsourcing perceived BE and sentiments at fine temporal and spatial scales in a timely manner. It contributes to engineering informatics by investigating the role of social media in environmental planning and proposing integrated domain-specific data analytic approaches for engineering practices.     

Survey of visual sentiment prediction for social media analysis

Recent years have witnessed a rapid spread of multi-modality microblogs like Twitter and Sina Weibo composed of image, text and emoticon. Visual sentiment prediction of such microblog based social media has recently attracted ever-increasing research focus with broad application prospect. In this paper, we give a systematic review of the recent advances and cutting-edge techniques for visual sentiment analysis. To this end, in this paper we review the most recent works in this topic, in which detailed comparison as well as experimental evaluation are given over the cutting-edge methods. We further reveal and discuss the future trends and potential directions for visual sentiment prediction.     

Computational approaches to subjectivity and sentiment analysis: Present and envisaged methods and applications

Recent years have witnessed a surge of interest in computational methods for affect, ranging from opinion mining, to subjectivity detection, to sentiment and emotion analysis. This article presents a brief overview of the latest trends in the field and describes the manner in which the articles contained in the special issue contribute to the advancement of the area. Finally, we comment on the current challenges and envisaged developments of the subjectivity and sentiment analysis fields, as well as their application to other Natural Language Processing tasks and related domains.     

An assessment of equivalence between paper and social media surveys: The role of social desirability and satisficing

Investigation of the underlying mechanisms responsible for measurement variance has received little attention. The primary objective of this study is to examine whether paper and social media surveys produce convergent results and investigate the underlying psychological mechanisms for the potential measurement nonequivalence. Particularly, we explored the role of social desirability and satisficing on the measurement results. We collected data via five different survey modes, including paper survey, ad hoc Web survey, online forum (message boards)-based, SNS-based and microblog-based surveys. The findings show that socially desirable responding does not lead to inconsistent results. Rather we found that satisficing causes inconsistent results in paper versus online surveys. Sociability reduces the possibility of engaging in satisficing that results in inconsistent results between traditional Web surveys and social media-based Web surveys.     

Exploring the relationships between self-presentation and self-esteem of mothers in social media in Russia

Majority of parents use social media platforms, with young mothers being the most active users. Academic research has only recently started addressing the impact of social media on mothers, although they are one of the most engaged online audiences. Instagram and Facebook perceived as positive types of social media, where users post positive content to increase encouraging response from their subscribers and thus enhance their self-esteem. This also relates to mothers portraying positive self-presentation online, therefore enhancing their parental self-esteem. This study provides in-depth analysis of 23 popular online profiles of mothers with more than thirty thousand followers on Instagram and 12 interviews with socially active mothers. This work focuses on mothers in Russia. Research findings show mothers with children of pre-school age are the most regular users of social media. This is due to time availability, as majority of these mothers are on maternity leave and due to little knowledge in child related aspects, which leads to lower self-esteem. They often look for assurance in online community. Mothers that are more confident have positive attitude towards social media communication. Mothers with initially lower self-esteem feel under pressure to maintain positive image to be in line with other mothers' presentation on social media. Mothers find Facebook more informative and supportive vehicle of communication than Instagram.     

Communication in the face of a school crisis: Examining the volume and content of social media mentions during active shooter incidents

Little is known about the effectiveness of social media in delivering information during active shooter incidents at the P-12 level. This study analyzed social media activity that occurred during and after two active shooter events on September 30, 2014. Over 5000 social media posts from Facebook, Twitter, blogs, and mainstream news outlets were analyzed. Social media analysis outlined the scope of online communication during the first week following the incidents, revealed social media frequency, increases in conversation, misinformation, and differences between parent and student posts. Results revealed spikes in social media chatter following the release of the identities of shooters and victims. Consistent with media dependency theory and the high levels of uncertainty characteristic of the incident, users’ social media posts contained more information than affect displays during the active shooter event. Implications for scholars and P-12 administrators are discussed.     

Enterprise reputation threats on social media: A case of data breach framing

Reputation threats on social media in the aftermath of a data breach is a critical concern to enterprises. We argue that any effort to minimize reputation threats will require an orderly assessment of how reputation threat manifests on social media. Drawing on crisis communication and social media literature, we analyze Twitter postings related to the 2014 Home Depot data breach. We identify a taxonomy of data breach frames and sub-frames and the related reputation threats as manifested by data breach responsibility-attributions and negative emotional responses. Results indicate that reputation threats vary for intentional, accidental, and victim data breach frames. Based on crisis stage theory, we also analyze the dynamics of evolving reputation threats as data breach situation unfolds on social media. Results suggest that the data breach frames and associated reputation threats vary across the crisis stages. Further, intentional and accidental frames increase subsequent responsibility-attributions and negative emotions. Tweets with responsibility-attributions further increase the subsequent generation of reputation-threatening tweets. Negative emotions, particularly anger and disgust, also increase subsequent reputation threats. Our study has implications for enterprise reputation management and word-of-mouth literature. The results yield valuable insights that can guide enterprise strategy for social media reputation management and post data breach intervention.     

The tweets that killed a university: A case study investigating the use of traditional and social media in the closure of a state university

The 12-month discussion surrounding a regional university campus quickly evolved from a suggestion of independence, to a plan, to the ultimate closure of the university. This unique series of events at the University of South Florida Polytechnic (USFP) allows for an investigation of how various forms of media were used during this significant event that impacted college student’s education and immediate future. A campus wide survey was combined with social and online media monitoring to assess the topics, authors, and methods used during prominent discussions during and preceding the closure of USFP. Although social media played a crucial role, the most common format was Twitter and it was used almost exclusively by members of the media itself. Students instead relied on traditional sources to gather information. Additionally, students expressed their opinion utilizing classic methods, such as petitions, foregoing more modern Twitter or Facebook campaigns. It is incorrect to automatically assume younger demographic authorship or utilization of social media technology. Whereas social media use could expand even more over the next decade, identifying authorship remains critical as it is unclear how frequent social media is viewed as an official method of public discussion, especially when politics and higher education collide.