Like It or Not

Giachanou, Anastasia; Crestani, Fábio

doi:10.1145/2938640

Cited by 439 publications

(125 citation statements)

References 72 publications

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“…Despite the unstructured nature of its text, tweets have become an interesting and popular topic of research in social media analysis over the past few years and have been widely studied by companies, marketers and even political analysts (Giachanou and Crestani 2016;Wu, Song, and Huang 2016).…”

Section: Sentiment and Time Series Analysis Of Twitter Datamentioning

confidence: 99%

Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media

Ibrahim

Wang

2019

Computers in Human Behavior

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Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media. Computers in Human Behavior, 96,[32][33][34][35][36][37][38][39][40][41][42][43][44][45]. https://doi. AbstractThe Twittersphere often offers valuable information about current events. However, despite the enormous quantity of tweets regarding online retailing, we know little about customers' perceptions regarding the products and services offered by online retail brands. Therefore, this study focuses on analysing brand-related tweets associated with five leading UK online retailers during the most important sales period of the year, covering Black Friday, Christmas and the New Year's sales events.We explore trends in customer tweets by utilising a combination of data analytics approaches including time series analysis, sentiment analysis and topic modelling to analyse the trends of tweet volume and sentiment and to understand the reasons underlying changes in sentiment. Through the sentiment and time series analyses, we identify several critical time points that lead to significant deviations in sentiment trends. We then use a topic modelling approach to examine the tweets in the period leading up to and following these critical moments to understand what exactly drives these changes in sentiment. The study provides a deeper understanding of online retailing customer behaviour and derives significant managerial insights that are useful for improving online retailing service provision.Ibrahim, Wang, and Bourne 2017). In addition, an overwhelmingly large volume of data (Netzer, Feldman, Goldenberg, and Fresko 2012) and a lack of practical tools with which to analyse unstructured data (Archak, Ghose, and Ipeirotis 2011) also complicate such analysis. Since Twitter content is produced abundantly every day, changes in customer sentiment and the relationship between those changes and emerging issues on Twitter are difficult to capture. The root cause of such changes may lie in a range of factors, including external events, accidents and disasters, and users' daily lives.Therefore, to understand what exactly drives changes in customer sentiment, this study attempts to exploit the content of these real-time data to understand trends in customer tweets. Thus, the research questions are as follows:• What are the volume and sentiment trends of online retailing brand-related tweets on Twitter?• What are the main reasons behind the changes in the volume and sentiment trends of online retail brand-related tweets?• How can online retailers learn from social media users to improve their online retailing service provision?To answer these questions, we analyse changes in customer sentiment and the underlying customer concerns that evolve through the time series. We examine those tweets or comments posted by customers that are directly related to five leading UK retailers namely Amazon, Argos, Asda, John Lewis and Tesco during the period spanning Black Friday, Christmas, Boxing Day and New Year's Day in the United Kingdom. Using a ...

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Section: Sentiment and Time Series Analysis Of Twitter Datamentioning

confidence: 99%

Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media

Ibrahim

Wang

2019

Computers in Human Behavior

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“…Considering the considerable potential for social media marketing, plenty of interest is now being focused on the spreading mechanism in social networks 3–6 . Some involve analyzing large amounts of empirical data 7–11 , and others formulate predictions of the popularity of a particular piece of information 12–14 .…”

Section: Introductionmentioning

confidence: 99%

Mechanistic modelling of viral spreading on empirical social network and popularity prediction

Feng

Lai

2018

Sci Rep

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Online social networks are becoming major platforms for people to exchange opinions and information. While spreading models have been used to study the dynamics of spreading on social networks, the actual spreading mechanism on social networks may be different from these previous models due to users’ limited attention and heterogeneous interests. The tractability of the spreading process in social networks allows us to develop a detailed and realistic model accounting for these factors. In addition, the empirical social networks have higher order correlations among node degrees, especially for directed networks like Twitter, that could affect the dynamics of spreading. Based on the analysis of the retweet process in the empirical Twitter network, we find both non-trivial correlations in network structures and non-standard spreading mechanisms for viral tweets. In particular, there is a strong evidence of information overload for retweeting behaviors that is not in line with the standard spreading model like the SIR (Susceptible, Infectious and Recovered) model, and can be described by a sublinear function. From these empirical findings, we introduce an intrinsic variable “attractiveness” to the message, describing the overall propensity for any node to retweet the message, and present the analytical equations to solve such an empirical process, validated through numerical simulations. The results from our model is consistent with findings from the empirical Twitter data. Our analysis also indicates a close relationship between the spreading sub-network structure and the final popularity of the information, leading to a method to predict the popularity of tweets more accurately than existing models.

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“…Breaking down arguments deriving from web or from casual discourse is a demanding task with doubts if it is even feasible. An evidence of the previous statement is the fact that the field of opinion mining thrives in social media data [32,33] and especially in twitter [34,35], on the contrary only limited research has been conducted on AM in unstructured data and fewer frameworks have been designed able to capture the special features of social media. In 5.1 we propose a conceptual framework able to capture the specific features of social media text and also enhance other tasks in the wider area of NLP with the use of argumentative features.…”

Section: Introductionmentioning

confidence: 99%

The evolution of argumentation mining: From models to social media and emerging tools

Lytos

Λάγκας²,

Sarigiannidis

et al. 2019

Information Processing & Management

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Argumentation mining is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. The initial approaches on modeling arguments was aiming to identify a flawless argument on specific fields (Law, Scientific Papers) serving specific needs (completeness, effectiveness). With the emerge of Web 2.0 and the explosion in the use of social media both the diffusion of the data and the argument structure have changed. In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media. We review, compare, and classify existing approaches, techniques and tools, identifying the positive outcome of combining tasks and features, and eventually propose a conceptual architecture framework. The proposed theoretical framework is an argumentation mining scheme able to identify the distinct sub-tasks and capture the needs of social media text, revealing the need for adopting more flexible and extensible frameworks.

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Like It or Not

Cited by 439 publications

References 72 publications

Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media

Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media

Mechanistic modelling of viral spreading on empirical social network and popularity prediction

The evolution of argumentation mining: From models to social media and emerging tools

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