Automatically Identifying Complaints in Social Media
Table 5: Group text features associated with tweets that are complaints and not complaints. Features are sorted by Pearson correlation (r) between their each feature’s normalized frequency and the outcome. We restrict to only the top six categories for each feature type. All correlations are significant at p
['[BOLD] Complaints  [BOLD] Label', '[BOLD] Complaints  [BOLD] Words', '[BOLD] Complaints  [ITALIC] r', '[BOLD] Not Complaints  [BOLD] Label', '[BOLD] Not Complaints  [BOLD] Words', '[BOLD] Not Complaints  [ITALIC] r']
The top features for the LIWC categories and Word2Vec topics are presented in Table 5. [CONTINUE] the top LIWC category (NEGATE). [CONTINUE] a cluster (Issues) contain words referring to issues or errors. [CONTINUE] Complaints tend to not contain personal pronouns (he, she, it, him, you, SHEHE, MALE, FEMALE), as the focus on expressing the complaint is on the self and the party the complaint is addressed to and not other third parties. [CONTINUE] Complaints are not usually accompanied by exclamation marks. [CONTINUE] General topics typical of complaint tweets include requiring assistance or customer support. Several groups of words are much more likely to appear in a complaint, although not used to express complaints per se: about orders or deliveries (in the retail domain), about access (in complaints to service providers) and about parts of tech products (in tech).