-
Thelwall, M.; Buckley, K.; Paltoglou, G.: Sentiment in Twitter events (2011)
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- Abstract
- The microblogging site Twitter generates a constant stream of communication, some of which concerns events of general interest. An analysis of Twitter may, therefore, give insights into why particular events resonate with the population. This article reports a study of a month of English Twitter posts, assessing whether popular events are typically associated with increases in sentiment strength, as seems intuitively likely. Using the top 30 events, determined by a measure of relative increase in (general) term usage, the results give strong evidence that popular events are normally associated with increases in negative sentiment strength and some evidence that peaks of interest in events have stronger positive sentiment than the time before the peak. It seems that many positive events, such as the Oscars, are capable of generating increased negative sentiment in reaction to them. Nevertheless, the surprisingly small average change in sentiment associated with popular events (typically 1% and only 6% for Tiger Woods' confessions) is consistent with events affording posters opportunities to satisfy pre-existing personal goals more often than eliciting instinctive reactions.
- Date
- 22. 1.2011 14:27:06
- Source
- Journal of the American Society for Information Science and Technology. 62(2011) no.2, S.406-418
- Type
- a
-
Thelwall, M.; Buckley, K.; Paltoglou, G.; Cai, D.; Kappas, A.: Sentiment strength detection in short informal text (2010)
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- Abstract
- A huge number of informal messages are posted every day in social network sites, blogs, and discussion forums. Emotions seem to be frequently important in these texts for expressing friendship, showing social support or as part of online arguments. Algorithms to identify sentiment and sentiment strength are needed to help understand the role of emotion in this informal communication and also to identify inappropriate or anomalous affective utterances, potentially associated with threatening behavior to the self or others. Nevertheless, existing sentiment detection algorithms tend to be commercially oriented, designed to identify opinions about products rather than user behaviors. This article partly fills this gap with a new algorithm, SentiStrength, to extract sentiment strength from informal English text, using new methods to exploit the de facto grammars and spelling styles of cyberspace. Applied to MySpace comments and with a lookup table of term sentiment strengths optimized by machine learning, SentiStrength is able to predict positive emotion with 60.6% accuracy and negative emotion with 72.8% accuracy, both based upon strength scales of 1-5. The former, but not the latter, is better than baseline and a wide range of general machine learning approaches.
- Date
- 22. 1.2011 14:29:23
- Footnote
- Vgl. auch das Erratum in: Journal of the American Society for Information Science and Technology. 62(2011) no.2, S.419
- Source
- Journal of the American Society for Information Science and Technology. 61(2010) no.12, S.2544-2558
- Type
- a
-
Thelwall, M.; Buckley, K.; Paltoglou, G.: Sentiment strength detection for the social web (2012)
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- Abstract
- Sentiment analysis is concerned with the automatic extraction of sentiment-related information from text. Although most sentiment analysis addresses commercial tasks, such as extracting opinions from product reviews, there is increasing interest in the affective dimension of the social web, and Twitter in particular. Most sentiment analysis algorithms are not ideally suited to this task because they exploit indirect indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms used to process social web texts can identify spurious sentiment patterns caused by topics rather than affective phenomena. This article assesses an improved version of the algorithm SentiStrength for sentiment strength detection across the social web that primarily uses direct indications of sentiment. The results from six diverse social web data sets (MySpace, Twitter, YouTube, Digg, Runners World, BBC Forums) indicate that SentiStrength 2 is successful in the sense of performing better than a baseline approach for all data sets in both supervised and unsupervised cases. SentiStrength is not always better than machine-learning approaches that exploit indirect indicators of sentiment, however, and is particularly weaker for positive sentiment in news-related discussions. Overall, the results suggest that, even unsupervised, SentiStrength is robust enough to be applied to a wide variety of different social web contexts.
- Source
- Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.163-173
- Type
- a
-
Paltoglou, G.; Salampasis, M.; Satratzemi, M.: ¬A results merging algorithm for distributed information retrieval environments that combines regression methodologies with a selective download phase (2008)
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- Abstract
- The problem of results merging in distributed information retrieval environments has gained significant attention the last years. Two generic approaches have been introduced in research. The first approach aims at estimating the relevance of the documents returned from the remote collections through ad hoc methodologies (such as weighted score merging, regression etc.) while the other is based on downloading all the documents locally, completely or partially, in order to calculate their relevance. Both approaches have advantages and disadvantages. Download methodologies are more effective but they pose a significant overhead on the process in terms of time and bandwidth. Approaches that rely solely on estimation on the other hand, usually depend on document relevance scores being reported by the remote collections in order to achieve maximum performance. In addition to that, regression algorithms, which have proved to be more effective than weighted scores merging algorithms, need a significant number of overlap documents in order to function effectively, practically requiring multiple interactions with the remote collections. The new algorithm that is introduced is based on adaptively downloading a limited, selected number of documents from the remote collections and estimating the relevance of the rest through regression methodologies. Thus it reconciles the above two approaches, combining their strengths, while minimizing their drawbacks, achieving the limited time and bandwidth overhead of the estimation approaches and the increased effectiveness of the download. The proposed algorithm is tested in a variety of settings and its performance is found to be significantly better than the former, while approximating that of the latter.
- Source
- Information processing and management. 44(2008) no.4, S.1580-1599
- Type
- a
-
Paltoglou, G.: Sentiment-based event detection in Twitter (2016)
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- Abstract
- The main focus of this article is to examine whether sentiment analysis can be successfully used for "event detection," that is, detecting significant events that occur in the world. Most solutions to this problem are typically based on increases or spikes in frequency of terms in social media. In our case, we explore whether sudden changes in the positivity or negativity that keywords are typically associated with can be exploited for this purpose. A data set that contains several million Twitter messages over a 1-month time span is presented and experimental results demonstrate that sentiment analysis can be successfully utilized for this purpose. Further experiments study the sensitivity of both frequency- or sentiment-based solutions to a number of parameters. Concretely, we show that the number of tweets that are used for event detection is an important factor, while the number of days used to extract token frequency or sentiment averages is not. Lastly, we present results focusing on detecting local events and conclude that all approaches are dependant on the level of coverage that such events receive in social media.
- Source
- Journal of the Association for Information Science and Technology. 67(2016) no.7, S.1576-1587
- Type
- a
