Robust Sense-Based Sentiment Classification

The new trend in sentiment classification is to use semantic features for representation of documents. We propose a semantic space based on WordNet senses for a supervised document-level sentiment classifier. Not only does this show a better performance for sentiment classification, it also opens opportunities for building a robust sentiment classifier. We examine the possibility of using similarity metrics defined on WordNet to address the problem of not finding a sense in the training corpus. Using three popular similarity metrics, we replace unknown synsets in the test set with a similar synset from the training set. An improvement of 6.2 % is seen with respect to baseline using this approach.

Sentiment Analysis : A Literature Survey

Our day-to-day life has always been influenced by what people think. Ideas and opinions of others have always affected our own opinions. The explosion of Web 2.0 has led to increased activity in Podcasting, Blogging, Tagging, Contributing to RSS, Social Bookmarking, and Social Networking. As a result there has been an eruption of interest in people to mine these vast resources of data for opinions. Sentiment Analysis or Opinion Mining is the computational treatment of opinions, sentiments and subjectivity of text. In this report, we take a look at the various challenges and applications of Sentiment Analysis. We will discuss in details various approaches to perform a computational treatment of sentiments and opinions. Various supervised or data-driven techniques to SA like Na\"ive Byes, Maximum Entropy, SVM, and Voted Perceptrons will be discussed and their strengths and drawbacks will be touched upon. We will also see a new dimension of analyzing sentiments by Cognitive Psychology mainly through the work of Janyce Wiebe, where we will see ways to detect subjectivity, perspective in narrative and understanding the discourse structure. We will also study some specific topics in Sentiment Analysis and the contemporary works in those areas

Robust Sense-Based Sentiment Classification

The new trend in sentiment classification is to use semantic features for representation of documents. We propose a semantic space based on WordNet senses for a supervised document-level sentiment classifier. Not only does this show a better performance for sentiment classification, it also opens opportunities for building a robust sentiment classifier. We examine the possibility of using similarity metrics defined on WordNet to address the problem of not finding a sense in the training corpus. Using three popular similarity metrics, we replace unknown synsets in the test set with a similar synset from the training set. An improvement of 6.2 % is seen with respect to baseline using this approach.