Literature Review on Sentiment Analysis of Big Data using Machine Learning

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Research areas:
Year:
2014
Type of Publication:
Article
Keywords:
Big Data Analysis, Machine Learning, Support Vector Machine, Artificial Neural Network, Classification
Authors:
Seema Verma; Manisha Sharma; Divya Rajput; Vani Mittal; Rashika Singh
Journal:
IJAIM
Volume:
3
Number:
3
Pages:
90-95
Month:
November
Abstract:
Sentiment analysis or Opinion Mining is the study of human emotions, feelings, opinions etc expressed in the form of text. It is one of the most progressive areas of research in natural language processing and text mining in recent times. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. As we all know, the Web holds an enormous amount of opinionated data which if correctly scrutinized, can significantly help business and society to a great extend. With the help of Supervised Machine Learning that involves Artificial Neural Network (ANN) and Support Vector Machine (SVM) one can easily perform classification of sentiments for both real time and archived data. This Literature focuses on combining the sturdiness of both the techniques (ANN and SVM) for various kinds of data available. The present literature review evaluates the prior work for the accuracy, running time, training time of machine learning (ANN and SVM) classification techniques.
Full text: IJAIM_364_Final.pdf

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