Comparing Grounded Theory and Topic Modeling: Extreme Divergence or Unlikely Convergence?

Researchers in information science and related areas have developed various methods for analyzing textual data, such as survey responses. This article describes the application of analysis methods from two distinct fields, one method from interpretive social science and one method from statistical machine learning, to the same survey data. The results show that the two analyses produce some similar and some complementary insights about the phenomenon of interest, in this case, nonuse of social media. We compare both the processes of conducting these analyses and the results they produce to derive insights about each method\u27s unique advantages and drawbacks, as well as the broader roles that these methods play in the respective fields where they are often used. These insights allow us to make more informed decisions about the tradeoffs in choosing different methods for analyzing textual data. Furthermore, this comparison suggests ways that such methods might be combined in novel and compelling ways

Using Sentiment Analysis to track reaction to the Global Game Jam Theme published in Proceedings of the International Conference on Game Jams, Hackathons, and Game Creation Events

In this paper, we examine the Global Game Jam Theme and the reaction of the 'jammers' to the release. The Theme is one of the main drivers for creative aspect of the Game Jam, it sets the tone of the games that are developed at the Jam. This paper introduces an experiment which uses 'sentiment analysis' to gauge the positive or negative reaction to the theme over the last 7 years of the Global Game Jam. The results of this study show that the 2012 theme had the the highest sentiment. Finally, we suggest that the 'sentiment analysis' or 'context analysis' could be used to gather data sets for other studies such as development practices

An Ontology Artifact for Information Systems Sentiment Analysis

As companies and organizations increasingly rely on on-line, user-supplied data to obtain valuable insights into their operations, sentiment analysis of textual data has proven to be a most valuable resource. To understand how sentiment analysis can be used effectively, it is important to identify what types of sentiment analysis could be employed during the analysis of a given situation. This research proposes an Information Systems Sentiment Ontology, the purpose of which is to provide a basis for mining and understanding sentiment, specifically from text provided by customers as online content. The Information Systems Sentiment Ontology is developed by analyzing the literature on emotion, sentiment analysis, and ontology development and from prior research on online forum analysis. A traditional design science approach is followed to the ontology development. Details on the creation and application of the ontology artifact are provided

Understanding, Discovering, and Mitigating Habitual Smartphone Use in Young Adults

People, especially young adults, often use their smartphones out of habit: They compulsively browse social networks, check emails, and play video-games with little or no awareness at all. While previous studies analyzed this phenomena qualitatively, e.g., by showing that users perceive it as meaningless and addictive, yet our understanding of how to discover smartphone habits and mitigate their disruptive effects is limited. Being able to automatically assess habitual smartphone use, in particular, might have different applications, e.g., to design better “digital wellbeing” solutions for mitigating meaningless habitual use. To close this gap, we first define a data analytic methodology based on clustering and association rules mining to automatically discover complex smartphone habits from mobile usage data. We assess the methodology over more than 130,000 phone usage sessions collected from users aged between 16 and 33, and we show evidence that smartphone habits of young adults can be characterized by various types of links between contextual situations and usage sessions, which are highly diversified and differently perceived across users. We then apply the proposed methodology in Socialize, a digital wellbeing app that (i) monitors habitual smartphone behaviors in real time and (ii) uses proactive notifications and just-in-time reminders to encourage users to avoid any identified smartphone habits they consider as meaningless. An in-the-wild study with 20 users (ages 19–31) demonstrates that Socialize can assist young adults in better controlling their smartphone usage with a significant reduction of their unwanted smartphone habits

Forecasting power of social media sentiment time series

Social media are not only the new form of communication, but also give the ability to big industry players, like Facebook, to analyze overwhelming amounts of data about customers’ behavior. The focus of this study was to analyze if social media time series have the power to predict the evolution of financial markets. Despite the short time frame being analyzed, the study delivered promising results that social media time series may be a leading indicator for market behavior after special events. Building on my findings, an applied sentiment trading strategy delivered positive abnormal returns and statistically significant positive alpha in and out-of-sample

Analysis of Students Emotion for Twitter Data using Naïve Bayes and Non Linear Support Vector Machine Approachs

Students' informal discussions on social media (e.g Twitter, Facebook) shed light into their educational understandings- opinions, feelings, and concerns about the knowledge process. Data from such surroundings can provide valuable knowledge about students learning. Examining such data, however can be challenging. The difficulty of students' experiences reflected from social media content requires human analysis. However, the growing scale of data demands spontaneous data analysis techniques. The posts of engineering students' on twitter is focused to understand issues and problems in their educational experiences. Analysis on samples taken from tweets related to engineering students' college life is conducted. The proposed work is to explore engineering students informal conversations on Twitter in order to understand issues and problems students encounter in their learning experiences. The encounter problems of engineering students from tweets such as heavy study load, lack of social engagement and sleep deprivation are considered as labels. To classify tweets reflecting students' problems multi-label classification algorithms is implemented. Non Linear Support Vector Machine, Naïve Bayes and Linear Support Vector Machine methods are used as multilabel classifiers which are implemented and compared in terms of accuracy. Non Linear SVM has shown more accuracy than Naïve Bayes classifier and linear Support Vector Machine classifier. The algorithms are used to train a detector of student problems from tweets. DOI: 10.17762/ijritcc2321-8169.150515