Mining the Web: Discovering Knowledge from HyperText Data
Mining the Web: Discovering Knowledge from HyperText Data
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Tracking Information Epidemics in Blogspace
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
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
Introduction to Information Retrieval
Introduction to Information Retrieval
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Expert Systems with Applications: An International Journal
Social Media Marketing: An Hour a Day
Social Media Marketing: An Hour a Day
Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web
Management Science
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Identifying breakpoints in public opinion
Proceedings of the First Workshop on Social Media Analytics
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In recent years, analysis of opinions from social web has witnessed a boom merely because such a volume of opinions is difficult to obtain through any other normal means of collecting opinion like surveys, polls etc. The analysis is interesting but at the same time difficult because of the sheer volume of information that the social media generates on the internet and the range of opinions possible to be expressed. Twitter is one such social media where opinions expressed play a significant influence on the marketability of a product. Hence, an accurate method for predicting sentiments could enable us to understand customers' preferences, their views on the product and services offered by the companies. These have the potential to create positive or negative wave in the market. Hence, this information is valuable for both companies and consumers. Enterprises can leverage this information for formalizing their strategies. The main focus of this paper is to analyze the sentiments expressed on Hollywood movies on Twitter so that customers' opinions, habits and preferences are extracted, analyzed and used to understand the market behaviour for better customer experience.