+ Jan Žižka František Dařena Department of Faculty of Business Informatics and Economics Mendel Czech University Republic in Brno MINING SIGNIFICANT WORDS FROM CUSTOMER OPINIONS WRITTEN IN DIFFERENT NATURAL LANGUAGES
+ Introduction Many companies collect opinions expressed by their customers. These opinions can hide valuable knowledge. Discovering the knowledge by people can be sometimes a very demanding task because the opinion database can be very large, the customers can use different languages, the people can handle the opinions subjectively, sometimes additional resources (like lists of positive and negative words) might be needed.
+ Objective For answering the question “What is significant for including a certain opinion into one of categories like satisfied or dissatisfied customers?” automatically extract words significant for positive and negative customers opinions and to form not too large dictionaries of these words.
+ Data description Processed data included reviews of hotel clients collected from publicly available sources. The reviews were labeled as positive and negative. Reviews characteristics: more than 5,000,000 reviews, written in more than 25 natural languages, written only by real customers, based on a real experience, written relatively carefully but still containing errors that are typical for natural languages.
+ Review examples Positive The breakfast and the very clean rooms stood out as the best features of this hotel. Clean and moden, the great loation near station. Friendly reception! The rooms are new. The breakfast is also great. We had a really nice stay. Good location - very quiet and good breakfast. Negative High price charged for internet access which actual cost now is extreamly low. water in the shower did not flow away The room was noisy and the room temperature was higher than normal. The air conditioning wasnt working
+ Data preparation Data collection, cleaning (removing tags, non- letter characters), converting to upper-case. Transforming into the bag-of-words representation, term frequencies (TF) used as attribute values. Removing the words with global frequency < 2. Stemming, stopwords removing, spell checking, diacritics removal etc. were not carried out.
+ Data characteristics 1200000 1000000 800000number of reviews positive 600000 negative 400000 200000 0 English French Spanish German Italian Russian Japan Czech
+ Data characteristics 250000 200000number of unique words 150000 MinTF=1 MinTF=2 100000 50000 0 English German Japan French Spanish Italian Russian Czech
+ Finding the significant words Thanksto having a large collection of labeled examples a classifier that separates positive and negative reviews could be created. To reveal significant attributes (words) a decision tree was built using the tree-generating algorithm c5 (by R. Quinlan) based on entropy minimization. The goal was not to achieve the best classification accuracy but to find relevant attributes that contribute to assigning a text to a given class. The significant words appeared in the nodes of the decision tree.
+ Finding the significant words The classification accuracy which is proportional to the relevancy of words was between 83 – 93%. Thedecision tree mostly asked if the frequency was > 0 or = 0 (binary representation). Thedecision tree provides a list of about 200-300 words significant for classification from the sentiment perspective together with the significance (i.e. the frequency of using the words during classification) of the words. Only15 words for each language is presented together with their significance (column %).
+ Handling large collections For languages with large amount of reviews the datasets were randomly split into subsets consisting of 50,000 reviews because of memory requirements and a decision tree was created for each such subset. Each of the 50,000-sample subsets gave almost the same list of words. The relevancies of extracted words were averaged.
+ Conclusions A procedure how to apply computers, machine learning, and natural language processing areas to automatically find significant words was presented. From the total number of words (80,000–200,000) only about 200–300 were identified as significant. The simple, unified procedure worked well for many languages. Following research focuses on determining the strength of sentiment and on generating typical short phrases instead of only creating individual words. The procedure might be used during the marketing research or marketing intelligence, for filtering reviews, generating lists of key-words etc.
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