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Heart and Soul: Sentiment Strength Detection in the Social Web with SentiStrength 1
"... Emotions are important in communication to effectively convey messages and to understand reactions to messages. Large scale studies of communication need methods to detect sentiment in order to investigate or model the processes involved. This chapter describes the sentiment strength detection progr ..."
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Emotions are important in communication to effectively convey messages and to understand reactions to messages. Large scale studies of communication need methods to detect sentiment in order to investigate or model the processes involved. This chapter describes the sentiment strength detection program SentiStrength that was developed during the CyberEmotions project to detect the strength of sentiments expressed in social web texts. SentiStrength uses a lexical approach that exploits a list of sentiment-related terms and has rules to deal with standard linguistic and social web methods to express sentiment, such as emoticons, exaggerated punctuation and deliberate misspellings. This chapter also describes how SentiStrength can be refined for particular topics and contexts and how variants are created for different languages. The chapter also briefly describes some studies that have applied SentiStrength to analyse trends in Twitter and You Tube comments.
Penguins in Sweaters, or Serendipitous Entity Search on User-generated Content
"... In many cases, when browsing the Web users are searching for specific information or answers to concrete questions. Sometimes, though, users find unexpected, yet interesting and useful results, and are encouraged to explore further. What makes a result serendipitous? We propose to answer this questi ..."
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In many cases, when browsing the Web users are searching for specific information or answers to concrete questions. Sometimes, though, users find unexpected, yet interesting and useful results, and are encouraged to explore further. What makes a result serendipitous? We propose to answer this question by exploring the potential of entities extracted from two sources of user-generated content – Wikipedia, a user-curated online encyclopedia, and Yahoo! Answers, a more unconstrained question/answering forum – in promoting serendipitous search. In this work, the content of each data source is represented as an entity network, which is further enriched with metadata about sentiment, writing quality, and topical category. We devise an algorithm based on lazy random walk with restart to retrieve entity recommendations from the networks. We show that our method provides novel results from both datasets, compared to standard web search engines. However, unlike previous research, we find that choosing highly emotional entities does not increase user interest for many categories of entities, suggesting a more complex relationship between topic matter and the desirable metadata attributes in serendipitous search.
The role of emotions in contributors activity: A case study on the gentoo community
- in International Conference on Cloud and Green Computing
, 2013
"... We analyze the relation between the emotions and the activity of contributors in the Open Source Software project Gentoo. Our case study builds on extensive data sets from the project’s bug tracking platform bugzilla, to quantify the activity of contributors, and its mail archives, to quantify the e ..."
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We analyze the relation between the emotions and the activity of contributors in the Open Source Software project Gentoo. Our case study builds on extensive data sets from the project’s bug tracking platform bugzilla, to quantify the activity of contributors, and its mail archives, to quantify the emotions of contributors by means of sentiment analysis. The Gentoo project is known for a period of centralization within its bug triaging community. This was followed by considerable changes in community organization and performance after the sudden retirement of the central contributor. We analyze how this event correlates with the negative emotions, both in bilateral email discussions with the central contributor, and at the level of the whole community of contributors. We then extend our study to consider the activity patterns on Gentoo contributors in general. We find that contributors are more likely to become inactive when they express strong positive or negative emotions in the bug tracker, or when they deviate from the expected value of emotions in the mailing list. We use these insights to develop a Bayesian classifier that detects the risk of contributors leaving the project. Our analysis opens new perspectives for measuring online contributor motivation by means of sentiment analysis and for real-time predictions of contributor turnover in Open Source Software projects. 1
User Engagement in Online News: Under the Scope of Sentiment, Interest, Affect, and Gaze
, 2013
"... Online content providers, like news portals and social media platforms, constantly seek new ways to attract large shares of online attention by keeping their users engaged. A common challenge is to identify which aspects of online interaction influence user engagement the most. In this article, thro ..."
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Online content providers, like news portals and social media platforms, constantly seek new ways to attract large shares of online attention by keeping their users engaged. A common challenge is to identify which aspects of online interaction influence user engagement the most. In this article, through an analysis of a news article collection obtained from Yahoo! News, we demonstrate that news articles exhibit considerable variation in terms of the sentimentality and polarity of their content, depending on factors like news provider and genre. Moreover, through a laboratory study, we observe the effect of sentimentality and polarity of news and comments on a set of subjective and objective measures of engagement. In particular, we show that attention, affect, and gaze differ across news of varying interestingness. As part of our study, we also explore methods that exploit the sentiments expressed in user comments to reorder the lists of comments displayed in news pages. Our results indicate that user engagement can be anticipated if we account for the sentimentality and polarity of the content, as well as other factors that drive attention and inspire human curiosity. 1
Damping sentiment analysis in online communication: Discussions, monologs and dialogs
- In Computational Linguistics and Intelligent Text Processing
, 2013
"... Abstract. Sentiment analysis programs are now sometimes used to detect patterns of sentiment use over time in online communication and to help automated systems interact better with users. Nevertheless, it seems that no previous published study has assessed whether the position of individual texts w ..."
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Abstract. Sentiment analysis programs are now sometimes used to detect patterns of sentiment use over time in online communication and to help automated systems interact better with users. Nevertheless, it seems that no previous published study has assessed whether the position of individual texts within ongoing communication can be exploited to help detect their sentiments. This article assesses apparent sentiment anomalies in on-going communication – texts assigned significantly different sentiment strength to the average of previous texts – to see whether their classification can be improved. The results suggest that a damping procedure to reduce sudden large changes in sentiment can improve classification accuracy but that the optimal procedure will depend on the type of texts processed.
A product feature inference model for mining implicit customer preferences within large scale social media networks
- In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers
, 2015
"... The acquisition and mining of product feature data from on-line sources such as customer review websites and large scale social media networks is an emerging area of research. In many existing design methodologies that acquire product feature pref-erences form online sources, the underlying assumpti ..."
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The acquisition and mining of product feature data from on-line sources such as customer review websites and large scale social media networks is an emerging area of research. In many existing design methodologies that acquire product feature pref-erences form online sources, the underlying assumption is that product features expressed by customers are explicitly stated and readily observable to be mined using product feature extraction tools. In many scenarios however, product feature preferences expressed by customers are implicit in nature and do not directly map to engineering design targets. For example, a customer may implicitly state “wow I have to squint to read this on the screen”, when the explicit product feature may be a larger screen. The au-thors of this work propose an inference model that automatically assigns the most probable explicit product feature desired by a customer, given an implicit preference expressed. The algorithm iteratively refines its inference model by presenting a hypothesis and using ground truth data, determining its statistical validity. A case study involving smartphone product features expressed through Twitter networks is presented to demonstrate the effec-tiveness of the proposed methodology. 1
Approval of the thesis: EMOTION ANALYSIS OF TURKISH TEXTS BY USING MACHINE LEARNING METHODS
, 2012
"... Technical University by, Prof. Dr. Canan Özgen _________ ..."
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Searching for Interestingness in Wikipedia and Yahoo! Answers
"... In many cases, when browsing the Web, users are searching for specific information. Sometimes, though, users are also looking for something interesting, surprising, or entertaining. Serendipitous search puts interestingness on par with relevance. We investigate how interesting are the results one ca ..."
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In many cases, when browsing the Web, users are searching for specific information. Sometimes, though, users are also looking for something interesting, surprising, or entertaining. Serendipitous search puts interestingness on par with relevance. We investigate how interesting are the results one can obtain via serendipitous search, and what makes them so, by comparing entity networks extracted from two prominent social media sites, Wikipedia and Yahoo! Answers.
UT-DB: An Experimental Study on Sentiment Analysis in Twitter
"... This paper describes our system for participating SemEval2013 Task2-B (Kozareva et al., 2013): Sentiment Analysis in Twitter. Given a message, our system classifies whether the message is positive, negative or neutral sentiment. It uses a co-occurrence rate model. The training data are constrained t ..."
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This paper describes our system for participating SemEval2013 Task2-B (Kozareva et al., 2013): Sentiment Analysis in Twitter. Given a message, our system classifies whether the message is positive, negative or neutral sentiment. It uses a co-occurrence rate model. The training data are constrained to the data provided by the task organizers (No other tweet data are used). We consider 9 types of features and use a subset of them in our submitted system. To see the contribution of each type of features, we do experimental study on features by leaving one type of features out each time. Results suggest that unigrams are the most important features, bigrams and POS tags seem not helpful, and stopwords should be retained to achieve the best results. The overall results of our system are promising regarding the constrained features and data we use. 1
Driving Curiosity in Search with Large-scale Entity Networks
"... In many search scenarios, users sometimes find unexpected, yet interesting and useful results, which make them curious; they experience serendipity. This curiosity encourages them to explore further. We developed an entity search system designed to support such an experience. The system explores the ..."
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In many search scenarios, users sometimes find unexpected, yet interesting and useful results, which make them curious; they experience serendipity. This curiosity encourages them to explore further. We developed an entity search system designed to support such an experience. The system explores the potential of entities extracted from two of the most popular sources of user-generated content – Wikipedia, a user-curated online encyclopedia, and Yahoo Answers, a more unconstrained question & answering forum – in promoting serendipitous search. The content of each data source is represented as a large network of entities, enriched with metadata about sentiment, writing quality, and topical category. A lazy random walk with restart is implemented to retrieve entities from the networks for a given entity query. This paper discusses our work, focusing on our experience in designing, developing, and evalu-ating such a system. We also discuss the challenges in developing large-scale systems that aim to drive curiosity in search. DOI: 10.1145/2682914.2682919
