Educational Sentiments Web Content Extraction and Analysis for Opinion Mining using Clustering Techniques
Keywords:
Educational Data Mining, Learning Analytics, Sentimental Analysis, Opinion Mining, Classification, Clustering TechniquesAbstract
The number of data produced across the world is increasing and could keep on growing at an accelerating rate for years to come. At organizations total businesses, servers have been overflowing with usage logs, transaction records, message flows and business operations records, detector data along with cell device data. Given such large sizes of text data sets, mining programs, that arrange the writing data sets into structured knowledge, will enhance efficient document access. There`s a demand for a highly effective method to categorize and analyze web reviews being a significant statistics investigation. The task of refining and assessing such collective net information together is called opinion mining, also it is also called Sentiment Analysis. We compare clustering techniques such as EM, K-Means, and Farthest First – in density and distance modes – with a threshold partitioning technique on the resulting two-dimensional data. These clustering techniques are also enhanced with the use of histogram smoothing techniques. We then evaluate our approach using standard accuracy, precision and recall metrics.
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