The more broad technique of sentiment analysis - using natural language programming and other textual analysis methods to assign positive or negative sentiment scores, typically to posts on social media and forums - operates only at around 70% accuracy (though others have recently claimed rates of 80%).
With the help of the techniques on sentiment analysis, opinionated sentences referring to a specific feature are first identified from product online reviews.
The "Machine learning techniques for sentiment analysis" section of this paper provides the intuition behind the task of sentiment classification by leveraging the modeling of aforementioned four classifiers.
In this paper, we make a comparative study of the effectiveness of ensemble technique for sentiment classification.
Finally, some in-depth discussion is presented and conclusions are drawn about the effectiveness of ensemble technique for sentiment classification.
This work attempted to utilize four machine learning techniques for the task of sentiment analysis.
The machine learning techniques have improved accuracy of sentiment analysis and expedite automatic evaluation of data these days.
We hope that our tool will not only help researchers and practitioners for accessing and comparing a wide range of sentiment analysis techniques, but can also help towards the development of this research field as a whole.
Since micro-blogs possess unique characteristics of limited length, ambiguous/error-prone, rich of acronyms, slangs, and flexible and novel usages, these would hinder the exploitation of sentiment analysis techniques (Liu 2012) in general.
We hope our effort can not only help researchers and practitioners to compare a wide range of sentiment analysis techniques, but also help fostering new relevant research in this area with a rigorous scientific approach.
