Study of Learning Classifiers Over Review Text Dataset for Aspect Level Sentiment Analysis
Keywords:
Sentiment Analysis, Classification Algorithm, SVM, ANN, CNNAbstract
With the passage of time, public and consumer reviews on social media gained a lot of attention and have become the easiest way for quick judgment. Many studies have been conducted in this field of sentiment analysis. The need for textual mining or sentimental analysis was felt or increased suddenly due to the outbursts of the internet and various social media platforms being available for the public to express their views or opinions. Since the number of people using the internet is growing all the time, there are a lot of different points of view available online. Users can openly voice their opinions, provide star ratings, and write reviews for books or any products they have read or seen. The abundance of information available from unstructured data aids in many knowledge productions. In this case, the difficulty is to choose an effective categorization method where the imbalanced dataset can have a significant detrimental impact on the machine classifier model’s performance. In this research work, a compressive study has been done for three classifiers like Support Vector Machine (SVM), Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) considering with imbalanced dataset. Various experiments are conducted to visualize different performance measures like accuracy, precision, recall, F-measure, specificity and G-mean over the above classifier models.
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