Sentence examples for methods for sentiment from inspiring English sources

However, due to the subjective nature of the Twitter datasets, metaheuristic-based clustering methods outperforms the traditional methods for sentiment analysis.

There are a few other methods for sentiment detection proposed in the literature and not considered here.

Agarwal et al. [9] investigate minimum redundancy maximum relevancy (mRMR) and IG methods for sentiment classification using NBM and SVM classifiers.

In this paper, we proposed a new feature selection method query expansion ranking (QER) for the sentiment analysis and compared it with the common feature selection methods for sentiment classification, including DFD and OCFS, CHI2 and IG.

The contribution of QER is that, as it is shown in the experimental results, the method is both language and classifier independent and can select better features than other methods for sentiment analysis.

Information gain is one of the most common feature selection methods for sentiment analysis [3, 9, 19, 35], which measures the content of information obtained after knowing the value of a feature in a document.

Kwon et al. [74] proposed a novel method for sentiment trend analysis using ACO algorithm and SentiWordNet.

Since Deep Learning relatively recently becomes popular, additional work needs to be done to use hierarchical learning strategy as a method for sentiment analysis of Big Data.

Such methods allow for sentiment analysis ("is a text positive, negative or neutral?") and text classification ("is a given article news, entertainment or sport?"), among many others.

Next, using calculation methods for appropriate sentiment value, the whole micro-blog message's emotional values were obtained.

We present two new computational methods for inferring sentiment orientation of social media content which address these challenges.