Visual Twitter Analytics (Vista)

A comprehensive survey on sentiment analysis: Challenges and future insights

With the rise of social networks, people now express their sentiments more frequently and comfortably through their social media activities on different events, person, and every little thing surrounding them. This generates a lot of unstructured data; billions of users post tweets every day as a daily regime on Twitter itself. This has given rise to many texts classification and analysis tasks, Sentiment Analysis (SA) being one of them. Through SA, it is conferred whether the users have negative or positive orientations in their opinions; the results of this task are significantly useful for decision-makers in various fields. This paper presents various facets of SA, like the process followed in SA, levels, approaches, and sentences considered in SA. Aspects such as growth, techniques, the share of various platforms, and SA pipeline are also covered in this paper. At last, we have highlighted some major challenges in order to define future directions.

Information Diffusion Model in Twitter: A Systematic Literature Review

Information diffusion, information spread, and influencers are important concepts in many studies on social media, especially Twitter analytics. However, literature overviews on the information diffusion of Twitter analytics are sparse, especially on the use of continuous time Markov chain (CTMC). This paper examines the following topics: (1) the purposes of studies about information diffusion on Twitter, (2) the methods adopted to model information diffusion on Twitter, (3) the metrics applied, and (4) measures used to determine influencer rankings. We employed a systematic literature review (SLR) to explore the studies related to information diffusion on Twitter extracted from four digital libraries. In this paper, a two-stage analysis was conducted. First, we implemented a bibliometric analysis using VOSviewer and R-bibliometrix software. This approach was applied to select 204 papers after conducting a duplication check and assessing the inclusion–exclusion criteria. At this stage, we mapped the authors’ collaborative networks/collaborators and the evolution of research themes. Second, we analyzed the gap in research themes on the application of CTMC information diffusion on Twitter. Further filtering criteria were applied, and 34 papers were analyzed to identify the research objectives, methods, metrics, and measures used by each researcher. Nonhomogeneous CTMC has never been used in Twitter information diffusion modeling. This finding motivates us to further study nonhomogeneous CTMC as a modeling approach for Twitter information diffusion.

The Efficiency of Social Network Services Management in Organizations. An In-Depth Analysis Applying Machine Learning Algorithms and Multiple Linear Regressions

The objective of this work is to detect the variables that allow organizations to manage their social network services efficiently. The study, applying machine learning algorithms and multiple linear regressions, reveals which aspects of published content increase the recognition of publications through retweets and favorites. The authors examine (I) the characteristics of the content (publication volumes, publication components, and publication moments) and (II) the message of the content (publication topics). The research considers 21,771 publications and thirty-nine variables. The results show that the recognition obtained through retweets and favorites is conditioned both by the characteristics of the content and by the message of the content. The recognition through retweets improves when the organization uses links, hashtags, and topics related to gender equality, whereas the recognition through favorites increases when the organization uses original tweets, publications between 8:00 and 10:00 a.m. and, again, gender equality related topics. The findings of this research provide new knowledge about trends and patterns of use in social media, providing academics and professionals with the necessary guidelines to efficiently manage these technologies in the organizational field.

A Survey of Opinion Mining in Arabic

Opinion-mining or sentiment analysis continues to gain interest in industry and academics. While there has been significant progress in developing models for sentiment analysis, the field remains an active area of research for many languages across the world, and in particular for the Arabic language, which is the fifth most-spoken language and has become the fourth most-used language on the Internet. With the flurry of research activity in Arabic opinion mining, several researchers have provided surveys to capture advances in the field. While these surveys capture a wealth of important progress in the field, the fast pace of advances in machine learning and natural language processing (NLP) necessitates a continuous need for a more up-to-date literature survey. The aim of this article is to provide a comprehensive literature survey for state-of-the-art advances in Arabic opinion mining. The survey goes beyond surveying previous works that were primarily focused on classification models. Instead, this article provides a comprehensive system perspective by covering advances in different aspects of an opinion-mining system, including advances in NLP software tools, lexical sentiment and corpora resources, classification models, and applications of opinion mining. It also presents future directions for opinion mining in Arabic. The survey also covers latest advances in the field, including deep learning advances in Arabic Opinion Mining. The article provides state-of-the-art information to help new or established researchers in the field as well as industry developers who aim to deploy an operational complete opinion-mining system. Key insights are captured at the end of each section for particular aspects of the opinion-mining system giving the reader a choice of focusing on particular aspects of interest.

Retweet or like? That is the question

Purpose

Due to the size and importance of social media, user-generated content analysis is becoming a key factor for companies and brands across the world. By using Twitter messages’ content, the purpose of this paper is to identify which elements of the messages enable tweet diffusion and facilitate eWOM.

Design/methodology/approach

In total, 30,082 tweets collected from 10,120 Twitter users were classified based on four assorted brands. By comparing with multiple regression techniques high vs low purchase involvement and hedonic vs utilitarian products and using the theory of heuristic-systematic processing of information, the authors examine the causes of tweet diffusion.

Findings

The authors illustrate how the elements of a tweet (hashtags, mentions, links, sentiment or tweet length) influence its diffusion and popularity.

Research limitations/implications

This study validated the use of information processing theories in the social media field. The study showed a picture on how different Twitter elements influence eWOM and message diffusion under several purchase involvement situations.

Practical implications

The results of this study can help social media brand community managers of all types of companies on how to write their Twitter messages to obtain greater dissemination and popularity.

Originality/value

The study offers a unique deep brand analysis which helps brands and companies to understand their social media popularity in detail. Depending on product category, companies can achieve maximum social impact on Twitter by focusing on the interactivity items that will work best for their products or brands.

Do Twitter phenomena check-in popular venues on Foursquare too?

Purpose

The purpose of this study is to figure out the visiting behaviors of the users who have different characteristics on Twitter.

Design/methodology/approach

The visit history of users who share their Foursquare check-ins on Twitter and the characteristics of visited venues (category, check-in count, tip count, like count, rating, and price tier) was collected with Foursquare API. In addition, the number of followers, friends, tweets and favorite-count were collected via Twitter API. First, users were clustered according to their Twitter related attributes. After that, profiling was applied on clusters according to the characteristics of the venues that were visited by the users.

Findings

Clustering analysis generated three clusters, namely, ordinary, talkative and popular. For each cluster, the visited venues were investigated according to the price classification, check-in, like, tip counts and the categories. The users in ordinary class prefer cheaper venues rather than talkative and popular users. On the other hand, popular users prefer the venues with the highest average number of check-ins, likes and tip counts. The top two categories for all clusters are cafe and shopping mall.

Originality/value

This study differentiates from the other studies in the literature by examining the data from Twitter with clustering and profiling these clusters with Foursquare data to understand venue preferences of Twitter users having various characteristics. The findings of this study will provide new insights for business owners to understand the customers more comprehensively and design better marketing strategies.

Application of social media analytics: a case of analyzing online hotel reviews

Purpose

Online customer reviews could shed light into their experience, opinions, feelings, and concerns. To gain valuable knowledge about customers, it becomes increasingly important for businesses to collect, monitor, analyze, summarize, and visualize online customer reviews posted on social media platforms such as online forums. However, analyzing social media data is challenging due to the vast increase of social media data. The purpose of this paper is to present an approach of using natural language preprocessing, text mining and sentiment analysis techniques to analyze online customer reviews related to various hotels through a case study.

Design/methodology/approach

This paper presents a tested approach of using natural language preprocessing, text mining, and sentiment analysis techniques to analyze online textual content. The value of the proposed approach was demonstrated through a case study using online hotel reviews.

Findings

The study found that the overall review star rating correlates pretty well with the sentiment scores for both the title and the full content of the online customer review. The case study also revealed that both extremely satisfied and extremely dissatisfied hotel customers share a common interest in the five categories: food, location, rooms, service, and staff.

Originality/value

This study analyzed the online reviews from English-speaking hotel customers in China to understand their preferred hotel attributes, main concerns or demands. This study also provides a feasible approach and a case study as an example to help enterprises more effectively apply social media analytics in practice.

Public opinion dissemination on mobile internet- a case of Ebola

Purpose

This study aims to improve disaster management. Social media, particularly microblog, has become a new platform for public opinion dissemination. However, few studies have been conducted to explore the structure of public opinions, the approaches for facilitating the spread of public opinions and the results of public opinion dissemination in the context of mobile internet for the purpose of improving disaster management.

Design/methodology/approach

This paper chooses Ebola as the research topic and extracts 14,735 Ebola-related data items from Sina Microblogs to examine the information nodes of public opinion and the characteristics of propagation paths on mobile internet. Particularly, nodes of public opinion between mobile terminals and non-mobile terminals are compared.

Findings

The results of this paper reveal the characteristics of public opinion propagation on mobile internet and verify the effectiveness of public opinion propagation on mobile internet. This study shows that public opinions propagate quickly, widely and efficiently and further generate great impacts on mobile internet.

Research limitations/implications

The methods used in this study can be useful for the government agencies and other relevant organizations to monitor public opinions, identify issues and problems proactively and develop strategies in a more efficient manner to improve disaster management.

Practical implications

The results of this paper are helpful for related departments to monitor public opinions and to further improve disaster management.

Originality/value

This paper explores the mechanism of public opinion dissemination on mobile internet and further investigates how to improve disaster management through a case study related to Ebola.

Online Chinese discussions about the 2014 World Cup

Purpose

The purpose of this paper is to explore online discussions about the 2014 World Cup on the Chinese social media platform Sina Weibo. Because China did not qualify for the World Cup, the study focusses on the role of online discussions surrounding a worldwide international event from an outsider perspective. Doing so will uncover not only the depth of dialogue surrounding issues of nation and sport, but – perhaps more importantly – also aid in uncovering the utility of online platforms in creating online communities even among presumed outsiders.

Design/methodology/approach

A content analysis of the discussions on Sina Weibo is used to identify differences between fanship comments and non-fanship comments in terms of the focus of the content and the degree of valence.

Findings

Overall, fans were more likely than non-fans to use positive valence in their comments to enhance the value of the World Cup. Moreover, fans were also more likely to discuss topics closely related to the event itself, such as teams, athletes, and factual information/news about the World Cup, establishing identification with in-group participants. Moreover, the findings also imply important insight regarding electronic commerce opportunities.

Originality/value

This paper is among the first to investigate the online discussions about World Cup in China. Theoretically, this paper provides a comprehensive framework to examine the online discussions of mega-sporting events in China based on theories of social identity theory and nationalism. Practically, it provides baseline data for the sports industry and public relation practitioners to promote a sports event when the direct nationalistic interests are absent. Moreover, it also aids in uncovering the substantial changes in sports-related communication, experiences, and mediated participation.