Surender Reddy Yerva

École Polytechnique Fédérale de Lausanne

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Karl Aberer

École Polytechnique Fédérale de Lausanne

Zoltán Miklós

Centre National de la Recherche Scientifique

Hoyoung Jeung

Systems, Applications & Products in Data Processing (Australia)

Alexandru Tandrau

École Polytechnique Fédérale de Lausanne

Flavia Grosan

École Polytechnique Fédérale de Lausanne

Michele Catasta

Stanford University

Jonnahtan Saltarin

University of Bern

Gianluca Demartini

University of Queensland

Cooperative Institutions

École Polytechnique Fédérale de Lausanne

University of Queensland

École Normale Supérieure - PSL

Laboratoire d'Informatique Fondamentale de Lille

Constructor University

Systems, Applications & Products in Data Processing (Australia)

Systems, Applications & Products in Data Processing (United Kingdom)

Data61

Stanford University

Google (United States)

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What have fruits to do with technology?

Surender Reddy Yerva Zoltán Miklós Karl Aberer

Twitter is a micro-blogging service on the Web, where people can enter short messages, which then become visible to other users of the service. While the topics of these messages varies, there are a lot of messages where the users express their opinions about companies or products. Since the twitter service is very popular, the messages form a rich source of information for companies. They can learn with the help of data mining and sentiment analysis techniques, how their customers like their products or what is the general perception of the company. There is however a great obstacle for analyzing the data directly: as the company names are often ambiguous, one needs first to identify, which messages are related to the company. In this paper we address this question. We present various techniques to classify tweet messages, whether they are related to a given company or not, for example, whether a message containing the keyword "apple" is about the company Apple Inc.. We present simple techniques, which make use of company profiles, which we created semi-automatically from external Web sources. Our advanced techniques take ambiguity estimations into account and also automatically extend the company profiles from the twitter stream itself. We demonstrate the effectiveness of our methods through an extensive set of experiments.

10.1145/1988688.1988745

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Citations (23)

Social and Sensor Data Fusion in the Cloud

Surender Reddy Yerva Jonnahtan Saltarin Hoyoung Jeung Karl Aberer

This paper explores the potential of fusing social and sensor data in the cloud, presenting a practice - a travel recommendation system that offers the predicted mood information of people on where and when users wish to travel. The system is built upon a conceptual framework that allows to blend the heterogeneous social and sensor data for integrated analysis, extracting weather-dependant people's mood information from Twitter and meteorological sensor data streams. In order to handle massively streaming data, the system employs various cloud-serving systems, such as Hadoop, HBase, and GSN. Using this scalable system, we performed heavy ETL as well as filtering jobs, resulting in 12 million tweets over four months. We then derived a rich set of interesting findings through the data fusion, proving that our approach is effective and scalable, which can serve as an important basis in fusing social and sensor data in the cloud.

Sensor Fusion

10.1109/mdm.2012.52

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Cloud based social and sensor data fusion

Surender Reddy Yerva Hoyoung Jeung Karl Aberer

As mobile cloud computing facilitates a wide spectrum of smart applications, the need for fusing various types of data available in the cloud grows rapidly. In particular, social and sensor data lie at the core in such applications, but typically processed separately. This paper explores the potential of fusing social and sensor data in the cloud, presenting a practice — a travel recommendation system that offers the predicted mood information of people on where and when users wish to travel. The system is built upon a conceptual framework that allows to blend the heterogeneous social and sensor data for integrated analysis, extracting weather-dependent people's mood information from Twitter and meteorological sensor data streams. In order to handle massively streaming data, the system employs various cloud-serving systems, such as Hadoop, HBase, and GSN. Using this scalable system, we performed heavy ETL as well as filtering jobs, resulting in 12 million tweets over four months. We then derived a rich set of interesting findings through the data fusion, proving that our approach is effective and scalable, which can serve as an important basis in fusing social and sensor data in the cloud.

Sensor Fusion

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It was easy, when apples and blackberries were only fruits

Surender Reddy Yerva Zoltán Miklós Karl Aberer

Ambiguities in company names are omnipresent. This is not accidental, companies deliberately chose ambiguous brand names, as part of their marketing and branding strategy. This procedure leads to new challenges, when it comes to finding information about the company on the Web. This paper is concerned with the task of classifying Twitter messages, whether they are related to a given company: for example, we classify a set of twitter messages containing a keyword apple, whether a message is related to the company Apple Inc. Our technique is essentially an SVM classier, which uses a simple representation of relevant and irrelevant information in the form of keywords, grouped in specic profiles. We developed a simple technique to construct such classiers for previously unseen companies, where no training set is available, by training the meta-features of the classier with the help of a general testset. Our techniques show high accuracy figures over the WePS-3 dataset.

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Towards better entity resolution techniques for Web document collections

Surender Reddy Yerva Zoltán Miklós Karl Aberer

As person names are non-unique, the same name on different Web pages might or might not refer to the same real-world person. This entity identification problem is one of the most challenging issues in realizing the Semantic Web or entity-oriented search. We address this disambiguation problem, which is very similar to the entity resolution problem studied in relational databases, however there are also several differences. Most importantly Web pages often only contain partial or incomplete information about the persons, moreover the available information is very heterogeneous, thus we are only able to obtain some uncertain evidence about whether two names refer to the same person using similarity functions. These similarity functions capture some aspects of the similarities between Web-pages, where the names occur, thus they perform very differently for the different names. We analyze some data engineering techniques to cope with the limited accuracy of the similarity functions and to combine multiple functions. Even with our simple techniques we could demonstrate systematic performance improvements and produce comparable results to state-of-the-art methods.

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Identification

10.1109/icdew.2010.5452698

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Entity-based Classification of Twitter Messages

International journal of computer science and applications (2012)

Surender Reddy Yerva Zoltán Miklós Karl Aberer

Twitter is a popular micro-blogging service on theWeb, where people can enter short messages, which then become visible to some other users of the service. While the topics of these messages varies, there are a lot of messages where the users express their opinions about some companies or their products. These messages are a rich source of information for companies for sentiment analysis or opinion mining. There is however a great obstacle for analyzing the messages directly: as the company names are often ambiguous (e.g. apple, the fruit vs. Apple Inc.), one needs first to identify, which messages are related to the company. In this paper we address this question. We present various techniques for classifying tweet messages containing a given keyword, whether they are related to a particular company with that name or not. We first present simple techniques, which make use of company profiles, which we created semi-automatically from external Web sources. Our advanced techniques take ambiguity estimations into account and also automatically extend the company profiles from the twitter stream itself. We demonstrate the effectiveness of our methods through an extensive set of experiments. Moreover, we extensively analyze the sources of errors in the classification. The analysis not only brings further improvement, but also enables to use the human input more efficiently.

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TripEneer: User-Based Travel Plan Recommendation Application

Proceedings of the International AAAI Conference on Web and Social Media (2021)

Surender Reddy Yerva Flavia Grosan Alexandru Tandrau Karl Aberer

Current travel recommendation systems are helpful in addressing a traveler's information needs to certain extent, however, most of them fail to factor in the user in their recommendations. TripEneer proposes travel recommendations to a traveler by keeping the user preferences and constraints as first class citizens. We present an intuitive UI for helping users plan their travel trips quickly and easily. In the current demo we present various global and user-specific ranking models used for recommending travel destinations. Our preliminary evaluation showed that the users found the personalized recommendations, based on the user model, most useful.

10.1609/icwsm.v7i1.14366

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Entity disambiguation in tweets leveraging user social profiles

Surender Reddy Yerva Michele Catasta Gianluca Demartini Karl Aberer

Pervasive web and social networks are becoming part of everyone's life.Users through their activities on these networks are leaving traces of their expertise, interests and personalities.With the advances in Web mining and user modeling techniques it is possible to leverage the user social network activity history to extract the semantics of user-generated content.In this work we explore various techniques for constructing user profiles based on the content they publish on social networks.We further show that one of the advantages of maintaining social network user profiles is to provide the context for better understanding of microposts.We propose and experimentally evaluate different approaches for entity disambiguation in social networks based on syntactic and semantic features on top of two different social networks: a general-interest network (i.e., Twitter) and a domain-specific network (i.e., StackOverflow).We demonstrate how disambiguation accuracy increases when considering enriched user profiles integrating content from both social networks.

Entity linking

10.1109/iri.2013.6642462

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Entities on the Web

Surender Reddy Yerva

Keywords: entity ; entity resolution ; entity profiles ; recommendations ; semantic web ; twitter streams ; social networks ; machine learning ; online reputation management ; sentiment metrics ; user models These Ecole polytechnique federale de Lausanne EPFL, n° 5897 (2013)Programme doctoral Informatique, Communications et InformationFaculte informatique et communicationsInstitut d'informatique fondamentaleLaboratoire de systemes d'information repartisJury: M. Grossglauser (president), K. Balog, Z. Miklos, M. Rajman Public defense: 2013-8-22 Reference doi:10.5075/epfl-thesis-5897Print copy in library catalog Record created on 2013-08-13, modified on 2017-05-12

10.5075/epfl-thesis-5897

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TweetSpector

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (2012)

Surender Reddy Yerva Zoltán Miklós Flavia Grosan Alexandru Tandrau Karl Aberer

TweetSpector is a tool for demonstrating entity-based of retrieval of tweets. The various features of this tool include: entity profile creation, real-time tweet classification, active improvement of the created profiles through user feedback, and the dashboard displaying different metrics.

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10.1145/2348283.2348441

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Citations (4)