Abstract
Sentiment analysis is the process of extracting the opinion expressed in a piece of text to determine the writer’s attitude towards a topic, product or any service in general and classify it into classes such as positive, negative or neutral. Bag of Words is the traditional approach for text representation in Sentiment Analysis where text is represented as bag of its words. This approach represents the text by breaking the sentence into words disregarding other semantic information. A problem that occurs due to this representation is Polarity Shift problem. To address polarity shift problem a dual sentiment analysis (DSA) system is created. It looks at the reviews from both the sides i.e. positive and negative. The existing work on dual sentiment analysis includes techniques where dual training and dual prediction is performed. The proposed system is to enhance the classification performance of the existing system by applying different classifiers apart from those used in existing system to obtain better results. After classification of reviews into appropriate classes, various graphs are plotted based on different parameters to validate the results and determine the best classifier from the applied classifiers.
Highlights
In the past decade due to the rapid growth of internet a lot of business activities are taking place online
Many approaches have been implemented in literature for sentiment analysis and most of them use the Bag of Words approach to represent the text for analysis
Basant Agarwala & Namita Mittal [10] proposed a model for sentiment analysis where initially various features are extracted such as unigrams, bi-grams and dependency features from the text
Summary
In the past decade due to the rapid growth of internet a lot of business activities are taking place online. With the increase of online sales, the number of online reviews available for such products and services have increased This makes it very necessary to extract information from the huge amount of textual data. In this paper a technique called Enhanced Dual Sentiment Analysis is used which includes Dual training and Dual prediction for the text. The Enhanced Dual Sentiment Analysis(EDSA) focuses on improving the classification accuracy of the current system by performing objective and subjective analysis and performs sentiment analysis on only subjective reviews. It applies Maximum Entropy and Voted classifier apart from Naïve Bayes and Support Vector Machines and compares the results with existing system.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Inventive Engineering and Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
