References of "Guo, Siwen 50009755"
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See detailLooking into the Past: Evaluating the Effect of Time Gaps in a Personalized Sentiment Model
Guo, Siwen UL; Höhn, Sviatlana UL; Schommer, Christoph UL

in ACM/SIGAPP Symposium On Applied Computing, Limassol 8-12 April 2019 (2019, April)

This paper concerns personalized sentiment analysis, which aims at improving the prediction of the sentiment expressed in a piece of text by considering individualities. Mostly, this is done by relating ... [more ▼]

This paper concerns personalized sentiment analysis, which aims at improving the prediction of the sentiment expressed in a piece of text by considering individualities. Mostly, this is done by relating to a person’s past expressions (or opinions), however the time gaps between the messages are not considered in the existing works. We argue that the opinion at a specific time point is affected more by recent opinions that contain related content than the earlier or unrelated ones, thus a sentiment model ought to include such information in the analysis. By using a recurrent neural network with an attention layer as a basic model, we introduce three cases to integrate time gaps in the model. Evaluated on Twitter data with frequent users, we have found that the performance is improved the most by including the time information in the Hawkes process, and it is also more effective to add the time information in the attention layer than at the input. [less ▲]

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See detailTopic-based Historical Information Selection for Personalized Sentiment Analysis
Guo, Siwen UL; Höhn, Sviatlana UL; Schommer, Christoph UL

in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges 24-26 April 2019 (2019, April)

In this paper, we present a selection approach designed for personalized sentiment analysis with the aim of extracting related information from a user's history. Analyzing a person's past is key to ... [more ▼]

In this paper, we present a selection approach designed for personalized sentiment analysis with the aim of extracting related information from a user's history. Analyzing a person's past is key to modeling individuality and understanding the current state of the person. We consider a user's expressions in the past as historical information, and target posts from social platforms for which Twitter texts are chosen as exemplary. While implementing the personalized model PERSEUS, we observed information loss due to the lack of flexibility regarding the design of the input sequence. To compensate this issue, we provide a procedure for information selection based on the similarities in the topics of a user's historical posts. Evaluation is conducted comparing different similarity measures, and improvements are seen with the proposed method. [less ▲]

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See detailPersonalized Sentiment Analysis and a Framework with Attention-Based Hawkes Process Model
Guo, Siwen UL; Höhn, Sviatlana UL; Xu, Feiyu et al

in Agents and Artificial Intelligence (2019)

People use different words when expressing their opinions. Sentiment analysis as a way to automatically detect and categorize people’s opinions in text, needs to reflect this diversity and individuality ... [more ▼]

People use different words when expressing their opinions. Sentiment analysis as a way to automatically detect and categorize people’s opinions in text, needs to reflect this diversity and individuality. One possible approach to analyze such traits is to take a person’s past opinions into consideration. In practice, such a model can suffer from the data sparsity issue, thus it is difficult to develop. In this article, we take texts from social platforms and propose a preliminary model for evaluating the effectiveness of including user information from the past, and offer a solution for the data sparsity. Furthermore, we present a finer-designed, enhanced model that focuses on frequent users and offers to capture the decay of past opinions using various gaps between the creation time of the text. An attention-based Hawkes process on top of a recurrent neural network is applied for this purpose, and the performance of the model is evaluated with Twitter data. With the proposed framework, positive results are shown which opens up new perspectives for future research. [less ▲]

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See detailAn Approach to Incorporate Emotions in a Chatbot with Seq2Seq Model
Zheng, Yaqiong; Guo, Siwen UL; Schommer, Christoph UL

in Benelux Conference on Artificial Intelligence, ‘s-Hertogenbosch 8-9 November 2018 (2018, November)

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See detailA Bilingual Study for Personalized Sentiment Model PERSEUS
Guo, Siwen UL; Schommer, Christoph UL

Scientific Conference (2018, September 10)

This paper investigates the significance of analyzing language preferences in personalized sentiment analysis. Motivated by the considerable amount of text generated by multilingual speakers on social ... [more ▼]

This paper investigates the significance of analyzing language preferences in personalized sentiment analysis. Motivated by the considerable amount of text generated by multilingual speakers on social platforms, we focus on constructing a single model that is able to analyze sentiments in a multilingual environment. In particular, Twitter texts are used in this research where the choice of language can be switched at a message-, sentence-, word- or topic-level. To represent and analyze the text, we extract concepts and main topics from the text and apply a recurrent neural network with attention mechanism in order to learn the relation between the lexical choices and the opinions of each sentiment holder. The personalized sentiment model PERSEUS is applied as the central structure of this research. Moreover, a language index is added to each concept to enable multilingual analysis, which provides a solution for analyzing code-switching in the text as well. In this work, English and German are chosen for a pilot study, and an artificial corpus is created to evaluate the situation with multilingual speakers. [less ▲]

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See detailPERSEUS: A Personalization Framework for Sentiment Categorization with Recurrent Neural Network
Guo, Siwen UL; Höhn, Sviatlana; Xu, Feiyu et al

in International Conference on Agents and Artificial Intelligence , Funchal 16-18 January 2018 (2018, January)

This paper introduces the personalization framework PERSEUS in order to investigate the impact of individuality in sentiment categorization by looking into the past. The existence of diversity between ... [more ▼]

This paper introduces the personalization framework PERSEUS in order to investigate the impact of individuality in sentiment categorization by looking into the past. The existence of diversity between individuals and certain consistency in each individual is the cornerstone of the framework. We focus on relations between documents for user-sensitive predictions. Individual’s lexical choices act as indicators for individuality, thus we use a concept-based system which utilizes neural networks to embed concepts and associated topics in text. Furthermore, a recurrent neural network is used to memorize the history of user’s opinions, to discover user-topic dependence, and to detect implicit relations between users. PERSEUS also offers a solution for data sparsity. At the first stage, we show the benefit of inquiring a user-specified system. Improvements in performance experimented on a combined Twitter dataset are shown over generalized models. PERSEUS can be used in addition to such generalized systems to enhance the understanding of user’s opinions. [less ▲]

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See detailEmbedding of the Personalized Sentiment Engine PERSEUS in an Artificial Companion
Guo, Siwen UL; Schommer, Christoph UL

in International Conference on Companion Technology, Ulm 11-13 September 2017 (2017, September)

The term Artificial Companion has originally been introduced by Y. Wilks [1] as “...an intelligent and helpful cognitive agent, which appears to know its owner and their habits, chats to them and diverts ... [more ▼]

The term Artificial Companion has originally been introduced by Y. Wilks [1] as “...an intelligent and helpful cognitive agent, which appears to know its owner and their habits, chats to them and diverts them, assists them with simple tasks. . . ”. To serve the users’ interests by considering a personal knowledge is, furthermore, demanded. The following position paper takes this request as motivation for the embedding of the PERSEUS system, which is a personalized sentiment framework based on a Deep Learning approach. We discuss how such an embedding with a group of users should be realized and why the utilization of PERSEUS is beneficial. [less ▲]

Detailed reference viewed: 175 (55 UL)