This paper aims to sentiment analysis users evaluate products using lexical analysis method combined with machine learning. This study analyze the emotions of users through analyzing their comments and evaluations for information posted or shared about services and products at the Explorascience QuyNhon. The first we retrieve the data of the user's product review comments and then build a Vietnamese emotional dictionary using vocabulary-based methods by calculating the semantic value of words or phrases in documents, finally, using a machine learning model to analyze and evaluate emotions with two problems: classifying sentences with feelings or without emotions and classifying sentences with positive or negative emotions. With input is a set of raw Vietnamese comments of users our methods will return outputs are Vietnamese comments which have been classified into three categories: without, positive or negative emotions. The input data will be a set of Vietnamese comments that are then evaluated and then put into processing Vietnamese errors with accents, processing emoticons, processing stop words collectively referred to as preprocessing. After the preprocessing has been standardized, the system begins to extract the characteristics of each sentence based on the emotion dictionary and the factors affecting the emotions in the sentence. From the characteristics obtained, subjective classification and emotional classification of comment sets to finally output the set of comments are classified into three categories: without emotions, with positive emotions or with negative emotions by machine learning model.
Relation:
International Journal of Advances in Soft Computing & Its Applications 15(2)
