• Corpus ID: 6830063

Sentiment Strength Detection for the Social Web 1

@inproceedings{Thelwall2012SentimentSD,
  title={Sentiment Strength Detection for the Social Web 1},
  author={Mike A Thelwall and Kevan Buckley and Georgios Paltoglou},
  year={2012}
}
Mike Thelwall, Kevan Buckley, Georgios Paltoglou Statistical Cybermetrics Research Group, School of Technology, University of Wolverhampton, Wulfruna Street, Wolverhampton WV1 1SB, UK. E-mail: m.thelwall@wlv.ac.uk, K.A.Buckley@wlv.ac.uk , G.Paltoglou@wlv.ac.uk Tel: +44 1902 321470 Fax: +44 1902 321478 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… 

Figures and Tables from this paper

Dynamic Lexical Framework to Evaluate the Evolution of Emotions in Twitter

Empoweb 2.0 is introduced, a prototype for dynamic sentiment analysis of Twitter data, based on the ongoing COVID-19 pandemic showing promising results.

A survey on opinion mining and sentiment analysis: Tasks, approaches and applications

360 degree view of cross-domain opinion classification: a survey

An organized survey of SA (also known as opinion mining) containing approaches, datasets, languages, and applications used is presented to support researches to get a greater understanding on emerging trends and state-of-the-art methods to be applied for future exploration.

A review of open-source machine learning algorithms for twitter text sentiment analysis and image classification

A review of open-source machine learning algorithms, built using neural network-based frameworks such as TensorFlow and Keras, to serve as a benchmark for bespoke SA algorithms and empirical results suggest deep-learning model frameworks to outperform scikit-learn algorithms.

An Explorative Study on Sentiment Analysis

  • R. PriyaJ. Sathiaseelan
  • Computer Science
    2017 World Congress on Computing and Communication Technologies (WCCCT)
  • 2017
This paper presents the views of different authors on the various approaches and applications of sentiment analysis.

Compositional language processing for multilingual sentiment analysis

The contributions presented in the thesis have potential applications in the era of the Web 2.0 and social media, such as being able to determine what is the view of society about products, celebrities or events, identify their strengths and weaknesses or monitor how these opinions evolve over time.

Sentiment-Analysis for German Employer Reviews

This paper examines the possibilities of sentiment analysis performed on German employer reviews to conclude major obstacles, promising approaches and possible prospective research directions in the domain of employer reputation analysis.

References

SHOWING 1-10 OF 56 REFERENCES

Twitter as a Corpus for Sentiment Analysis and Opinion Mining

This paper shows how to automatically collect a corpus for sentiment analysis and opinion mining purposes and builds a sentiment classifier, that is able to determine positive, negative and neutral sentiments for a document.

Twitter, MySpace, Digg: Unsupervised Sentiment Analysis in Social Media

The results demonstrate that the proposed algorithm, even though unsupervised, outperforms machine learning solutions in the majority of cases, overall presenting a very robust and reliable solution for sentiment analysis of informal communication on the Web.

Data mining emotion in social network communication: Gender differences in MySpace

The extent to which emotion is present in MySpace comments is examined, using a combination of data mining and content analysis, and exploring age and gender to suggest females are more successful social network site users partly because of their greater ability to textually harness positive affect.

Automatic construction of a context-aware sentiment lexicon: an optimization approach

This paper proposes a novel optimization framework that provides a unified and principled way to combine different sources of information for learning a context-dependent sentiment lexicon that is not only domain specific but also dependent on the aspect in context given an unlabeled opinionated text collection.

Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena

It is speculated that large scale analyses of mood can provide a solid platform to model collective emotive trends in terms of their predictive value with regards to existing social as well as economic indicators.

Exploring the Sentiment Strength of User Reviews

This paper presents an approach to estimating the sentiment strength of user reviews according to the strength of adverbs and adjectives expressed by users in their opinion phrases and shows that the proposed approach is effective in the task of sentiment classification and achieves a good performance on a multi-scale evaluation.

Sentiment Analysis in the News

This work distinguishes three different possible views on newspaper articles ― author, reader and text, which have to be addressed differently at the time of analysing sentiment, and presents work on mining opinions about entities in English language news.

SentiFul: A Lexicon for Sentiment Analysis

The algorithm for automatic extraction of new sentiment-related compounds from WordNet is elaborated using words from SentiFul as seeds for sentiment-carrying base components and applying the patterns of compound formations.

SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining

This work discusses SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications, and reports on the improvements concerning aspect (b) that it embodies with respect to version 1.0.
...