The aim of the project is to experiment with different machine learning algorithms to predict the sentiment of unseen reviews from the improved corpus that has the additional sentiment information of all sub-phrases. We use the Rotten Tomatoes movie review corpus1 that has been greatly improved upon, and annotated with a fine sentiment score via Amazon Mechanical Turk. We want to see whether ha...

To deal with the uncertain network public sentiment emergency of which attribute represented by intuitionistic fuzzy language term, we present a new information entropy measure for intuitionistic fuzzy value. By using the entropy measure, the weight of each sentiment attribute of emergency can be determined. Then by means of the information fusion technique of intuitionistic fuzzy sentiment att...

Current sentiment analysis systems rely on static (context independent) sentiment lexica with proximity based fixed-point prior polarities. However, sentimentorientation changes with context and these lexical resources give no indication of which value to pick at what context. The general trend is to pick the highest one, but which that is may vary at context. To overcome the problems of the pr...

Sentiment Analysis is a natural language processing task that extracts sentiment from various text forms and classifies them according to positive, negative or neutral polarity. It analyzes emotions, feelings, and the attitude of a speaker or a writer towards a context. This paper gives comparative study of various sentiment classification techniques and also discusses in detail two main catego...

The paper gives an overview of the different sentiment classification approaches and tools used for sentiment analysis. Starting from this overview the paper provides a classification of (i) approaches with respect to features/techniques and advantages/limitations and (ii) tools with respect to the different techniques used for sentiment analysis. Different application fields of application of ...

We aim to model online news as an evolving self-replicating phenomenon under selection pressures due to the sentiment which it evokes. We explore this idea by applying lexicon-based sentiment classification to clusters of quotes created using the MemeTracker framework. We perform correlation tests on the popularity, sentiment and network features extracted from each cluster and found that senti...

Sentiment analysis of citations in scientific papers and articles is a new and interesting problem which can open up many exciting new applications in bibliographic search and bibliometrics. Current work on citation sentiment detection focuses on only the citation sentence. In this paper, we address the problem of context-enhanced citation sentiment detection. We present a new citation sentimen...

Sentiment analysis aims to determine the attitude and the feelings of the opinion holder for the given reviews. Reviews contain features and opinion. Automatic extraction of customer opinion which is used by both manufacturers and customers. The Sentiment Classifier might classify reviews as positive or negative based on the sentiment expressed in review. Sentiment classification is domain depe...

Sentiment analysis of documents aims to characterise the positive or negative sentiment expressed in documents. It has been formulated as a supervised classification problem, which requires large numbers of labelled documents. Semi-supervised sentiment classification using limited documents or words labelled with sentiment-polarities are approaches to reducing labelling cost for effective learn...

Sentiment trend of twitter users have a great influence on their friends and the crowd listened. This paper directs at the user sentiment state of twitter, the unique medium, and applies set pair analysis method for trend analysis. First, we begin with set pair contact degree, then based on set pair affective computing model to make comparison with the size relationship of same degree, differen...