Auflistung nach Schlagwort "sentiment analysis"
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- KonferenzbeitragAgile Sentiment Analysis for more Responsive Public Relations(Electronic Government and Electronic Participation - Joint Proceedings of Ongoing Research of IFIP EGOV and IFIP ePart 2030, 2013) Cestnik, Bojan; Kern, AlenkaThe constituting e-government organizations like ministries, agencies, funds and councils often have to deal with severe media attention and sometimes more or less justified criticism. In this paper we present an approach that supports the task of managing public relations (PR) between organizations and the news media. The proposed approach is based on agile sentiment analysis of the questions that organizations receive from the journalists and media. Such analysis is relevant for reviewing past events as well as predicting the future happenings. We demonstrate the utility of the proposed approach by analyzing a set of 298 questions received by a public organization from various media in the period from 2007 till 2012. The results confirm that that by incorporating agile sentiment analysis into regular PR workflow organizations can improve their understanding and control of communication with the media and public.
- KonferenzbeitragA Corpus of Memes from Reddit: Acquisition, Preparation and First Case Studies(INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Schmidt, Thomas; Schiller, Fabian; Götz, Matthias; Wolff, ChristianWe present a corpus of memes and their textual components that were acquired from the popular meme platform r\memes, a subreddit of Reddit and one of the major outlets of online meme culture. The corpus consists of the most popular memes from 2013-2021 on the platform and we acquired 11,701 memes and 280,351 text tokens. We conduct several case studies focused on diachronic analysis to highlight the possibilities of the corpus for research in internet studies and online culture. We examine the general activity on the platform throughout the years and identify a significant increase in meme production beginning 2017. Results of sentiment analysis show a tendency towards memes with positively classified texts. The analysis of most frequent words per half-year spotlights the importance of certain cultural events for meme culture (e.g. the 2016 US election). Using the LIWC to analyze swear and sexual words shows an overall decrease in the usage of these words pointing to an increased moderation of the platform. The corpus is publicly available for the research community for further studies.
- KonferenzbeitragImprovement of automated social media sentiment analysis methods - a context-based approach(SKILL 2019 - Studierendenkonferenz Informatik, 2019) Debeyem Dennis; Eder, Tim; Guigas, Paul Vincent; Schuberth, ViktoriaThe sentiment analysis of social media data increasingly gains importance in business and research. But still, topical algorithms cope with problems, since it is reasonably manageable to extract the tonality of a social media post, but not the authors attitude towards a given topic. However, in most cases, this is the relevant information users of social media analysis tools are looking for. To tackle this problem, we propose a context-based algorithm that not only focuses on isolated postings, but also takes the authorsŠ earlier postings and their interactions with other usersŠ posts into account to derive their actual opinion on a subject. To evaluate this approach, we implemented a test system and compared the algorithmŠs results to manually assessed sentiments.
- ZeitschriftenartikelInvestigating the Relationship Between Emotion Recognition Software and Usability Metrics(i-com: Vol. 19, No. 2, 2020) Schmidt, Thomas; Schlindwein, Miriam; Lichtner, Katharina; Wolff, ChristianDue to progress in affective computing, various forms of general purpose sentiment/emotion recognition software have become available. However, the application of such tools in usability engineering (UE) for measuring the emotional state of participants is rarely employed. We investigate if the application of sentiment/emotion recognition software is beneficial for gathering objective and intuitive data that can predict usability similar to traditional usability metrics. We present the results of a UE project examining this question for the three modalities text, speech and face. We perform a large scale usability test (N = 125) with a counterbalanced within-subject design with two websites of varying usability. We have identified a weak but significant correlation between text-based sentiment analysis on the text acquired via thinking aloud and SUS scores as well as a weak positive correlation between the proportion of neutrality in users’ voice and SUS scores. However, for the majority of the output of emotion recognition software, we could not find any significant results. Emotion metrics could not be used to successfully differentiate between two websites of varying usability. Regression models, either unimodal or multimodal could not predict usability metrics. We discuss reasons for these results and how to continue research with more sophisticated methods.
- KonferenzbeitragMeetings and Mood - Related or Not? Insights from Student Software Projects (Summary)(Software Engineering 2023, 2023) Klünder, Jil; Karras, OliverMeetings are part of most software projects which is why they have been frequently analyzed by researchers. Often, this research focuses on the interactions. We analyze meetings from a more abstract view by applying sentiment analysis to the statements made during the meeting. That is, we analyze whether the statements are positive, negative, or neutral, and how the statements made are related to the mood of a team before and after the meeting. Our results are based on insights from 21 student software projects and show some interesting findings, including that the amount of positive and negative statements during the meeting has no measurable influence on the mood afterwards. This summary refers to the paper “Meetings and Mood – Related or Not? Insights from Student Software Projects” [KK22]. This paper was published in the proceedings of the 16th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 2022.
- KonferenzbeitragQuantitative comparison of polarity lexicons in sentiment analysis tasks: Using a lexicon overlap score for similarity measurement between lexicons(SKILL 2020 - Studierendenkonferenz Informatik, 2020) Welter, Felix J.M.Sentiment classification is either based on sentiment lexicons or machine learning. For the construction and improvement of sentiment lexicons, several approaches and algorithms have been designed. The resulting lexicons are commonly benchmarked in different tasks and compared by their respective performance. However, this measure depends on the application domain. This work proposes a method for context-independent comparison of sentiment lexicons. Three scoring methods for similarity measurement of lexicons are explained. Furthermore, exemplarily applications of the scores are shown, including lexicon similarity analysis before and after expansion via a Distributional Thesaurus and clustering of lexicons. Adaptability and limitations of the lexicon overlap score and the demonstrated applications are discussed.
- KonferenzbeitragSentiBooks: Enhancing Audiobooks via Affective Computing and Smart Light Bulbs(Mensch und Computer 2019 - Tagungsband, 2019) Ortloff, Anna-Marie; Güntner, Lydia; Windl, Maximiliane; Schmidt, Thomas; Kocur, Martin; Wolff, ChristianWe present SentiBooks, a smartphone application to enhance the audiobook listening experience via affective computing and smart light bulbs. Users can connect to Philips Hue Light Bulbs with a smartphone app while listening to an audiobook. The app analyzes the emotional expression of the narrator of the audiobook using speech emotion recognition and adjusts the colors of the lighting settings according to the expression of the narrator in 10-seconds intervals. By transitioning between colors that are connected to the specific emotion that is currently dominant in the reading, the overall audiobook experience is intensified.