Sentence examples for classification of sentiments from inspiring English sources
Sentiment analysis is about the classification of sentiments expressed in review documents.
The experiment is carried out by using freeware WEKA software tool for classification of sentiments in the text.
The classification of sentiments in a review document is performed through identifying and separating all the positive and negative opinion words.
Sprenger et al. (2014b) collected Twitter data related to listed companies, used the Naive Bayes algorithm for text classification of market sentiments, and constructed a daily overall indicator to reflect those sentiments.
The third method-machine learning-uses computer algorithms for the classification of textual sentiment.
In addition, high overall accuracy in classification of textual sentiment has been achieved through the machine learning method.
Our goal is to examine whether these feature selection methods can reduce the feature sizes and improve the classification accuracy of sentiment analysis with respect to different document domains, languages, and classifiers.
Given the growing assortment of sentiment-measuring instruments, it is imperative to understand which aspects of sentiment dictionaries contribute to both their classification accuracy and their ability to provide richer understanding of texts.
There are a number of application examples pertaining to natural language processing, more specifically in the areas of sentiment classification, text classification, spam email detection, and multiple language text classification.
Finally, Fuzzy clustering algorithm is used to analyze sentiment classification of micro-blog users, and classification result is forecasted with tools such as SPSS, and sentiment trend charts are generated to understand users' emotion state more intuitively.
Valence judgment (or sentiment classification) of emotion words and emotion-laden words has been carried out in both lines of emotion studies, by psycholinguists and computer scientists, respectively.
