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sentiment analysis and classification based on textual reviews
02-21-2014, 09:33 AM
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sentiment analysis and classification based on textual reviews
Sentiment analysis focuses on the analysis and understanding of the emotions from the text patterns. It identifies the opinion or attitude that a person has towards a topic or an object and it seeks to identify the viewpoint underlying a text span. The sentiment analysis calculates the overall feeling or mood of consumers as reflected in social media toward a specific brand or company and determine whether they are viewed positively or negatively. This involves high cost in labor and time. The result predicted are not accurate and efficient. To avoid the time complexity and accuracy, the fuzzy classification algorithm with Natural Language Processing (NLP) Mechanism is used. NLP is used for POS-Tagging and parsing simultaneously. The proposed method has the sentence tokenize for different languages for the better accuracy and uses the feature extraction for most frequently occurring words in the corpus as polarity indicators. This proposed approach combines the natural language processing and fuzzy classification to improve the accuracy of sentiment prediction and reduce the space and time complexity by using stop word removal and stemming algorithm. A new algorithm called Sentiment Fuzzy Classification algorithm with parts of speech tags is used to improve the classification accuracy on the student staff feedbacks.(ie.., to classify the feedback given to staff ) and view it in graph format.
 


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