Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Cross-Language Information Retrieval
Cross-Language Information Retrieval
Automatic sentiment analysis using the textual pattern content similarity in natural language
TSD'10 Proceedings of the 13th international conference on Text, speech and dialogue
Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner
Clustering a very large number of textual unstructured customers' reviews in english
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
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Opinions expressed by text documents freely written in various natural languages represent a valuable source of knowledge that is hidden in large datasets. The presented research describes a text mining-method how to discover words that are significant for expressing different opinions (positive and negative). The method applies a simple but unified data pre-processing for all languages, providing the bag-of-words with words represented by their frequencies in the data. Then, the frequencies are used by the algorithm which generates decision trees. The tree decisive nodes contain the words that are significant for expressing the opinions. Positions of these words in the tree represent their significance degree, where the most significant word is in the node. As a result, a list of relevant words can be used for creating a dictionary containing only relevant information. The described method was tested using very large sets of customers' reviews concerning the on-line hotel room booking. For more than 15 languages, there were available several millions of reviews. The resulting dictionaries included only about 200 significant words.