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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Dmitrij Chusovlyanov
Machine Learning Techniques for Multi-class Opinion Mining Tasks
School of Applied Mathematics and Information Science
Bachelor’s programme
9
2014
AbstractThis paper examines two approaches to the problem of opinion mining: an approach based on machine learning methods and an approach based on the use of sentiment-lexicons. The method of automated extraction words from the text that carry emotional value was described and implemented. Subsequent text classification method based on the obtained sentiment-lexicons was described and implemented.For some standard machine learning techniques (such method is naive Bayes classification, support vector machines, Random Forest) it is proposed to use words from the obtained sentiment-lexicon as classification features.Methods are tested on real data –feedback and comments of Internet resource imhonet.ru user’s on three subject areas: books, movies and camera. The data were presented at the seminar on Russian Information Retrieval Evaluation Seminar (ROMIP).The optimal size of a sentiment-lexicon (which is automatically constructed) is experimentally determined and is based on sentiment-value, according to which words are ranked in the dictionary. The experiments revealed the optimal value of the sentiment value, ​​over which the part of the dictionary is cut-off. This eliminates from consideration part of the vocabulary with low concentration of sentiment words.The paper presents experiments aimed to compare the quality of constructed sentiment-lexicons for different domains and different sets of input data.For machine learning methods indicators of quality were calculated in addition to average ones (such as Accuracy, Macro_Precision, Macro_Recall). That allows us to compare machine learning methods and method based on sentiment lexicons.It is also possible to compare the machine-learning techniques, working with words from the dictionary as classification features, and the method of calculating the aggregated sentiment-based text value based on sentiment of the words in the text and belonging to the lexicon. Comparisons were made for both the average quality values ​​obtained testing machine learning techniques and sentiment approach, and for the separate methods. Key words: Opinion mining, machine learning, sentiment analysis, SVM, Random Forest, Naive Bayes, lexicon-based approach.

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