Methods and systems are provided for expanding an electronic word list, containing a set of words where each word is associated with a label from a first set of labels. A subset of training data containing a set of texts having a second set of labels is obtained. For each word in the electronic word list and a label in the sub-set of the training data, a feature selection criterion is calculated. One or more words are selected, for which resulting value of the feature selection criterion calculation is greater than a predetermined threshold value. The one or more selected words are added to the electronic word list.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for expanding an electronic word list, the method comprising: obtaining the electronic word list containing a set of words, wherein each word is associated with a label from a first set of labels; obtaining a subset of training data containing a set of texts having a second set of labels; for each word in the electronic word list and a label in the sub-set of the training data, calculating a feature selection criterion; selecting one or more words for which resulting value of the feature selection criterion calculation is greater than a predetermined threshold value; and adding the one or more selected words to the electronic word list. 2. The method of claim 1, wherein the second label set includes the first label set. 3. The method of claim 1, further comprising obtaining label mapping information, wherein the first label set is different from the second label set, and the label mapping information indicates a mapping between each label in the first label set and a corresponding label in the second label set. 4. The method of claim 1, wherein the first label set includes labels having numeric values. 5. The method of claim 1, wherein the first label set includes labels having text-based values. 6. The method of claim 1, wherein value of the feature selection criterion is calculated using a chi-square test. 7. The method of claim 1, wherein the step of calculating the feature selection criterion includes obtaining one or more additional feature selection criteria; calculating value of each criteria; normalizing the calculated values; and determining a maximum value from the normalized values. 8. The method of claim 1, wherein a weight is associated with each word in the electronic word list. 9. The method of claim 8, further comprising calculating a weight for each of the selected one or more words that is directly proportional to the value of the feature selection criteria and inversely proportion to a number of iteration. 10. The method of claim 1, wherein the step of obtaining the subset of training data comprises selecting texts from a training set that contain words from the electronic word list, wherein each of the selected text's labels matches a label of at least one word in the electronic word list. 11. The method of claim 1, further comprising analyzing text using the electronic word list having the one or more added words. 12. A system comprising: one or more data processors; and one or more storage devices storing instructions that, when executed by the one or more data processors, cause the one or more data processors to perform operations comprising: obtaining an electronic word list containing a set of words, wherein each word is associated with a label from a first set of labels; obtaining a subset of training data containing a set of texts having a second set of labels; for each word in the electronic word list and a label in the sub-set of the training data, calculating a feature selection criterion; selecting one or more words for which resulting value of the feature selection criterion calculation is greater than a predetermined threshold value; and adding the one or more selected words to the electronic word list. 13. The system of claim 12, wherein the second label set includes the first label set. 14. The system of claim 12, the operations further comprising obtaining label mapping information, wherein the first label set is different from the second label set, and the label mapping information indicates a mapping between each label in the first label set and a corresponding label in the second label set. 15. The system of claim 12, wherein the first label set includes labels having numeric values. 16. The system of claim 12, wherein the first label set includes labels having text-based values. 17. The system of claim 12, wherein value of the feature selection criterion is calculated using a chi-square test. 18. The system of claim 12, wherein the step of calculating the feature selection criterion includes: obtaining one or more additional feature selection criteria; calculating value of each criteria; normalizing the calculated values; and determining a maximum value from the normalized values. 19. The system of claim 12, wherein a weight is associated with each word in the electronic word list. 20. The system of claim 19, the operations further comprising calculating a weight for each of the selected one or more words that is directly proportional to the value of the feature selection criteria and inversely proportion to a number of iteration. 21. The system of claim 1, wherein the step of obtaining the subset of training data comprises: selecting texts from a training set that contain words from the electronic word list, wherein each of the selected text's label matches a label of at least one word in the electronic word list. 22. The system of claim 1, further comprising analyzing text using the electronic word list having the one or more added words. 23. A computer-readable storage medium having machine instructions stored therein, the instructions being executable by a processor to cause the processor to perform operations comprising obtaining an electronic word list containing a set of words, wherein each word is associated a label from a first set of labels; obtaining a subset of training data containing a set of texts having a second set of labels; for each word in the electronic word list and a label in the sub-set of the training data, calculating a feature selection criterion; selecting one or more words for which resulting value of the feature selection criterion calculation is greater than a predetermined threshold value; and adding the one or more selected words to the electronic word list.