Feature Extraction and Sentiment Analysis Using Machine Learning
The role of social networks has bought a tremendous change in the analysis of the opinions. Understanding people sentiments or opinion helps the business or organization to better understand their customers. There are several platforms where people can easily post their views about a service or products, these can be facebook, twitter e.t.c. Feature extraction or aspect extraction becomes important since one needs to know the qualities a product or a service have. In this research, we have analyzed hotel reviews by applying n-gram for feature. As the dataset is always noisy so basic preprocessing steps are applied before extraction. The features extracted are trained and tested by basic machine learning classifiers. Various machine learning algorithms like KNN, SVM, and random forest are used for the analysis of the performance. The evaluation measures are calculated at the end to validate the results. K-fold cross validation scheme is also applied on the dataset to improve the overall accuracy of the results.
Communications in Computer and Information Science
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Vaish, Neha; Goel, Nidhi; and Gupta, Gaurav, "Feature Extraction and Sentiment Analysis Using Machine Learning" (2022). Kean Publications. 813.