ThemeRiver: Visualizing Thematic Changes in Large Document Collections
IEEE Transactions on Visualization and Computer Graphics
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A decision tree of bigrams is an accurate predictor of word sense
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Visualising Sentiments in Financial Texts?
IV '05 Proceedings of the Ninth International Conference on Information Visualisation
Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
ARSA: a sentiment-aware model for predicting sales performance using blogs
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Review: The role of emotion in computer-mediated communication: A review
Computers in Human Behavior
Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums
ACM Transactions on Information Systems (TOIS)
Extracting Product Comparisons from Discussion Boards
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Beyond Microblogging: Conversation and Collaboration via Twitter
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
How valuable is medical social media data? Content analysis of the medical web
Information Sciences: an International Journal
Get out the vote: determining support or opposition from congressional floor-debate transcripts
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A machine learning approach to sentiment analysis in multilingual Web texts
Information Retrieval
Twitter power: Tweets as electronic word of mouth
Journal of the American Society for Information Science and Technology
Using text mining and sentiment analysis for online forums hotspot detection and forecast
Decision Support Systems
Sentiment analysis of Chinese documents: From sentence to document level
Journal of the American Society for Information Science and Technology
DASA: Dissatisfaction-oriented Advertising based on Sentiment Analysis
Expert Systems with Applications: An International Journal
Leveraging sentiment analysis for topic detection
Web Intelligence and Agent Systems
Fine-grained opinion mining by integrating multiple review sources
Journal of the American Society for Information Science and Technology
Twitter use by the U.S. Congress
Journal of the American Society for Information Science and Technology
Sentiment in short strength detection informal text
Journal of the American Society for Information Science and Technology
Aspect-based sentiment analysis of movie reviews on discussion boards
Journal of Information Science
Predicting consumer sentiments from online text
Decision Support Systems
Journal of the American Society for Information Science and Technology
Lexicon-based methods for sentiment analysis
Computational Linguistics
Networked Politics on Cyworld: The Text and Sentiment of Korean Political Profiles
Social Science Computer Review
Mining sentiments in SMS texts for teaching evaluation
Expert Systems with Applications: An International Journal
Election Forecasts With Twitter: How 140 Characters Reflect the Political Landscape
Social Science Computer Review
Manipulation of online reviews: An analysis of ratings, readability, and sentiments
Decision Support Systems
Feature-based opinion mining and ranking
Journal of Computer and System Sciences
Expert Systems with Applications: An International Journal
Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications
Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications
Blog mining-review and extensions: "From each according to his opinion"
Decision Support Systems
Natural Language Processing in Game Studies Research: An Overview
Simulation and Gaming
A lexicon model for deep sentiment analysis and opinion mining applications
Decision Support Systems
From once upon a time to happily ever after: Tracking emotions in mail and books
Decision Support Systems
Making objective decisions from subjective data: Detecting irony in customer reviews
Decision Support Systems
Vehicle defect discovery from social media
Decision Support Systems
Hi-index | 12.05 |
Blogs and social networks have recently become a valuable resource for mining sentiments in fields as diverse as customer relationship management, public opinion tracking and text filtering. In fact knowledge obtained from social networks such as Twitter and Facebook has been shown to be extremely valuable to marketing research companies, public opinion organizations and other text mining entities. However, Web texts have been classified as noisy as they represent considerable problems both at the lexical and the syntactic levels. In this research we used a random sample of 3516 tweets to evaluate consumers' sentiment towards well-known brands such as Nokia, T-Mobile, IBM, KLM and DHL. We used an expert-predefined lexicon including around 6800 seed adjectives with known orientation to conduct the analysis. Our results indicate a generally positive consumer sentiment towards several famous brands. By using both a qualitative and quantitative methodology to analyze brands' tweets, this study adds breadth and depth to the debate over attitudes towards cosmopolitan brands.