62 research outputs found

    MISNIS: an intelligent platform for Twitter topic mining

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    Twitter has become a major tool for spreading news, for dissemination of positions and ideas, and for the commenting and analysis of current world events. However, with more than 500 million tweets flowing per day, it is necessary to find efficient ways of collecting, storing, managing, mining and visualizing all this information. This is especially relevant if one considers that Twitter has no ways of indexing tweet contents, and that the only available categorization “mechanism” is the #hashtag, which is totally dependent of a user's will to use it. This paper presents an intelligent platform and framework, named MISNIS - Intelligent Mining of Public Social Networks’ Influence in Society - that facilitates these issues and allows a non-technical user to easily mine a given topic from a very large tweet's corpus and obtain relevant contents and indicators such as user influence or sentiment analysis. When compared to other existent similar platforms, MISNIS is an expert system that includes specifically developed intelligent techniques that: (1) Circumvent the Twitter API restrictions that limit access to 1% of all flowing tweets. The platform has been able to collect more than 80% of all flowing portuguese language tweets in Portugal when online; (2) Intelligently retrieve most tweets related to a given topic even when the tweets do not contain the topic #hashtag or user indicated keywords. A 40% increase in the number of retrieved relevant tweets has been reported in real world case studies. The platform is currently focused on Portuguese language tweets posted in Portugal. However, most developed technologies are language independent (e.g. intelligent retrieval, sentiment analysis, etc.), and technically MISNIS can be easily expanded to cover other languages and locations

    Analyzing microblogs with affinity propagation

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    Recently, there has been a great deal of interest in analyz-ing inherent structures in posts on microblogs such as Twit-ter. While many works utilize a well-known topic modeling technique, we instead propose to apply Affinity Propaga-tion [4] (AP) to analyze such a corpus, and we hypothesize that AP may provide different perspective to the traditional approach. Our preliminary analysis raises some interesting facts and issues, which suggest future research directions

    A multi-resolution approach to learning with overlapping communities

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    The recent few years have witnessed a rapid surge of par-ticipatory web and social media, enabling a new laboratory for studying human relations and collective behavior on an unprecedented scale. In this work, we attempt to harness the predictive power of social connections to determine the preferences or behaviors of individuals such as whether a user supports a certain political view, whether one likes one product, whether he/she would like to vote for a presidential candidate, etc. Since an actor is likely to participate in mul-tiple different communities with each regulating the actor’s behavior in varying degrees, and a natural hierarchy might exist between these communities, we propose to zoom into a network at multiple different resolutions and determine which communities are informative of a targeted behavior. We develop an efficient algorithm to extract a hierarchy of overlapping communities. Empirical results on several large-scale social media networks demonstrate the superiority of our proposed approach over existing ones without consider-ing the multi-resolution or overlapping property, indicating its highly promising potential in real-world applications

    The best of both worlds: highlighting the synergies of combining manual and automatic knowledge organization methods to improve information search and discovery.

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    Research suggests organizations across all sectors waste a significant amount of time looking for information and often fail to leverage the information they have. In response, many organizations have deployed some form of enterprise search to improve the 'findability' of information. Debates persist as to whether thesauri and manual indexing or automated machine learning techniques should be used to enhance discovery of information. In addition, the extent to which a knowledge organization system (KOS) enhances discoveries or indeed blinds us to new ones remains a moot point. The oil and gas industry was used as a case study using a representative organization. Drawing on prior research, a theoretical model is presented which aims to overcome the shortcomings of each approach. This synergistic model could help to re-conceptualize the 'manual' versus 'automatic' debate in many enterprises, accommodating a broader range of information needs. This may enable enterprises to develop more effective information and knowledge management strategies and ease the tension between what arc often perceived as mutually exclusive competing approaches. Certain aspects of the theoretical model may be transferable to other industries, which is an area for further research

    Opinion mining: Reviewed from word to document level

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    International audienceOpinion mining is one of the most challenging tasks of the field of information retrieval. Research community has been publishing a number of articles on this topic but a significant increase in interest has been observed during the past decade especially after the launch of several online social networks. In this paper, we provide a very detailed overview of the related work of opinion mining. Following features of our review make it stand unique among the works of similar kind: (1) it presents a very different perspective of the opinion mining field by discussing the work on different granularity levels (like word, sentences, and document levels) which is very unique and much required, (2) discussion of the related work in terms of challenges of the field of opinion mining, (3) document level discussion of the related work gives an overview of opinion mining task in blogosphere, one of most popular online social network, and (4) highlights the importance of online social networks for opinion mining task and other related sub-tasks

    Analyzing Tweets For Predicting Mental Health States Using Data Mining And Machine Learning Algorithms

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    Tweets are usually the outcome of peoples’ feelings on various topics. Twitter allows users to post casual and emotional thoughts to share in real-time. Around 20% of U.S. adults use Twitter. Using the word-frequency and singular value decomposition methods, we identified the behavior of individuals through their tweets. We graded depressive and anti-depressive keywords using the tweet time-series, time-window, and time-stamp methods. We have collected around four million tweets since 2018. A parameter (Depressive Index) is computed using the F1 score and Mathews correlation coefficient (MCC) to indicate the depressive level. A framework showing the Depressive Index and the Happiness Index is prepared with the time, location, and keywords and delivers F1 Score, MCC, and CI values. COVID-19 changed the routines of most peoples\u27 lives and affected mental health. We studied the tweets and compared them with the COVID-19 growth. The Happiness Index from our work and World Happiness Report for Georgia, New York, and Sri Lanka is compared. An interactive framework is prepared to analyze the tweets, depict the happiness index, and compare it. Bad words in tweets are analyzed, and a map showing the Happiness Index is computed for all the US states and was compared with WalletHub data. We add tweets continuously and a framework delivering an atlas of maps based on the Happiness Index and make these maps available for further study. We forecasted tweets with real-time data. Our results of tweets and COVID-19 reports (WHO) are in a similar pattern. A new moving average method was presented; this unique process gave perfect results at peaks of the function and improved the error percentage. An interactive GUI portal computes the Happiness Index, depression index, feel-good- factors, prediction of the keywords, and prepares a Happiness Index map. We plan to create a public web portal to facilitate users to get these results. Upon completing the proposed GUI application, the users can get the Happiness Index, Depression Index values, Happiness map, and prediction of keywords of the desired dates and geographical locations instantaneously

    Twitter and society

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    Energy Data Analytics for Smart Meter Data

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    The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal
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