Impact of Reddit Discussions on Use or Abandonment of Wearables

Discussion platform, Reddit, is the third most visited website in the US. People can post their questions on this platform to get varying opinions from fellow users, which in turn might also influence their behavior and choices. Wearables are becoming widely adopted, yet challenges persist in their effective long term use because of technical and device related, or personal issues. Therefore, by employing sentiment analysis, this paper aims to analyze how decisions of use or abandonment of wearables are influenced by discussions on Reddit. The results are based on the analysis of 6680 posts and their associated 50,867 comments posted between December 2015 - December 2017 on the subreddit (user created groups) on android wear. Our results show that sentiment of the discussion is majorly dictated by the sentiment of the post itself, and people decide to continue using their devices when fellow Redditors offer them workarounds, or the discussion receives majority of positive or fact-driven neutral comments

Détection de contradiction dans les commentaires

L'analyse des avis (commentaires) générés par les utilisateurs devient de plus en plus exploitable par une variété d'applications. Elle permet de suivre l'évolution des avis ou d'effectuer des enquêtes sur des produits. La détection d'avis contradictoires autour d'une ressource Web (ex. cours, film, produit, etc.) est une tâche importante pour évaluer cette dernière. Dans cet article, nous nous concentrons sur le problème de détection des contradictions et de la mesure de leur intensité en se basant sur l'analyse du sentiment autour des aspects spécifiques à une ressource (document). Premièrement, nous identifions certains aspects, selon les distributions des termes émotionnels au voisinage des noms les plus fréquents dans l'ensemble des commentaires. Deuxièmement, nous estimons la polarité de chaque segment de commentaire contenant un aspect. Ensuite, nous prenons uniquement les ressources contenant ces aspects avec des polarités opposées (positive, négative). Troisièmement, nous introduisons une mesure de l'intensité de la contradiction basée sur la dispersion conjointe de la polarité et du rating des commentaires contenant les aspects au sein de chaque ressource. Nous évaluons l'efficacité de notre approche sur une collection de MOOC (Massive Open Online Courses) contenant 2244 cours et leurs 73873 commentaires, collectés à partir de Coursera. Nos résultats montrent l'efficacité de l'approche proposée pour capturer les contradictions de manière significative

Modeling Interaction Features for Debate Side Clustering

Online discussion forums are popular social media platforms for users to express their opinions and discuss controversial issues with each other. To automatically identify the sides/stances of posts or users from textual content in forums is an important task to help mine online opinions. To tackle the task, it is important to exploit user posts that implicitly contain support and dispute (interaction) information. The challenge we face is how to mine such interaction information from the content of posts and how to use them to help identify stances. This paper proposes a two-stage solution based on latent variable models: an interaction feature identification stage to mine interaction features from structured debate posts with known sides and reply intentions; and a clustering stage to incorporate interaction features and model the interplay between interactions and sides for debate side clustering. Empirical evaluation shows that the learned interaction features provide good insights into user interactions and that with these features our debate side model shows significant improvement over other baseline methods. Copyright is held by the owner/author(s).EI

A unified latent variable model for contrastive opinion mining

There are large and growing textual corpora in which people express contrastive opinions about the same topic. This has led to an increasing number of studies about contrastive opinion mining. However, there are several notable issues with the existing studies. They mostly focus on mining contrastive opinions from multiple data collections, which need to be separated into their respective collections beforehand. In addition, existing models are opaque in terms of the relationship between topics that are extracted and the sentences in the corpus which express the topics; this opacity does not help us understand the opinions expressed in the corpus. Finally, contrastive opinion is mostly analysed qualitatively rather than quantitatively. This paper addresses these matters and proposes a novel unified latent variable model (contraLDA), which: mines contrastive opinions from both single and multiple data collections, extracts the sentences that project the contrastive opinion, and measures the strength of opinion contrastiveness towards the extracted topics. Experimental results show the effectiveness of our model in mining contrasted opinions, which outperformed our baselines in extracting coherent and informative sentiment-bearing topics. We further show the accuracy of our model in classifying topics and sentiments of textual data, and we compared our results to five strong baselines

Review on recent advances in information mining from big consumer opinion data for product design

In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design

Anatomy of a Social Media Movement: Diffusion, Sentiment and Network Analysis

Social media has increased the availability of abundant user interaction data. Technology-mediated social participation tools like Twitter can inform us about collective actions and social movement mobilization. Current focus of social media and social movement research are on usage and impact of technology during historical uprisings. But online social networks are participatory mediums, and filled up with multi-dimensional user interactions, which requires more concrete attentions and need investigations at granular levels. Moreover, limited attention has been paid on how activists develop online social networks. This study stressed on Twitter’s ability of helping in making sense of online debates and present meaningful descriptions about social events. It focused on a specific social media movement and investigated on what were protesters’ behaviors and opinions on Twitter, the structures of their online networks, leadership roles, and information diffusion patterns. This study took mixed methods approach with combination of sentiment analysis, content analysis, social network analysis, and time series analysis. During the social movement, people’s sentiment took a range of emotional levels including anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. Their opinions expressed political biasness. The study revealed that protesters broadcast information worldwide, and during digital activism they formed leaderships even on Twitter’s horizontal structural platform. Twitter activists exposed a long-tail information sharing culture. Strong-ties formed small-world network while weak-ties stayed on peripheries