The nature of statistical learning theory
The nature of statistical learning theory
Automatic resource compilation by analyzing hyperlink structure and associated text
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Evaluating strategies for similarity search on the web
Proceedings of the 11th international conference on World Wide Web
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Mining anchor text for query refinement
Proceedings of the 13th international conference on World Wide Web
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
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
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
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
An effective statistical approach to blog post opinion retrieval
Proceedings of the 17th ACM conference on Information and knowledge management
Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web
Management Science
A survey on sentiment detection of reviews
Expert Systems with Applications: An International Journal
A study of inter-annotator agreement for opinion retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Reading the markets: forecasting public opinion of political candidates by news analysis
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Classifying sentiment in microblogs: is brevity an advantage?
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Integrating interactivity into visualising sentiment analysis of blogs
Proceedings of the first international workshop on Intelligent visual interfaces for text analysis
Identifying and following expert investors in stock microblogs
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
SoMEST: a model for detecting competitive intelligence from social media
Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments
Survey on mining subjective data on the web
Data Mining and Knowledge Discovery
Visualizing sentiments in business-customer relations with metaphors
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Online debate summarization using topic directed sentiment analysis
Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining
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While most work in sentiment analysis in the financial domain has focused on the use of content from traditional finance news, in this work we concentrate on more subjective sources of information, blogs. We aim to automatically determine the sentiment of financial bloggers towards companies and their stocks. To do this we develop a corpus of financial blogs, annotated with polarity of sentiment with respect to a number of companies. We conduct an analysis of the annotated corpus, from which we show there is a significant level of topic shift within this collection, and also illustrate the difficulty that human annotators have when annotating certain sentiment categories. To deal with the problem of topic shift within blog articles, we propose text extraction techniques to create topic-specific sub-documents, which we use to train a sentiment classifier. We show that such approaches provide a substantial improvement over full documentclassification and that word-based approaches perform better than sentence-based or paragraph-based approaches.