Figure 1: Flow of information in AutoTag
This paper addresses the task of automatic assignment of tags to weblog posts; while some work on weblog classification exists, we are not aware of published work about tag discovery. To this end, we describe a system--AutoTag--that, given a weblog post, offers a small number of tags which seem useful for it; the blogger then reviews the suggestions, selecting those which she finds instrumental. More than just simplifying the tagging process, AutoTag also improves its quality: first, by increasing the chance that weblog posts will be tagged in the first place, and second by offering relevant tags that may have not been applied otherwise. This in turn improves the tasks for which tagging is aimed at, providing better search and browse capabilities.
An application of collaborative filtering methods to automated tag discovery becomes clear when the ``user'' and ``product'' concepts are examined from a different angle. In AutoTag, the blog posts themselves take the role of users, and the tags assigned to them function as the products that the users expressed interest in. In traditional recommender systems, similar users are assumed to buy similar products; AutoTag makes the same assumption, and identifies useful tags for a post by examining tags assigned to similar posts. Just as with traditional recommender systems, the recommendations are then further improved by incorporating external knowledge about the bloggers, the posts, or the tags.
The different stages of the tag suggestion process in AutoTag are shown in Figure 1. Once the user supplies a weblog post, posts which are similar to it are identified. Next, the tags assigned to these posts are aggregated, creating a ranked list of likely tags. In the next stage, AutoTag filters and reranks this tag list; finally, the top-ranked tags are offered to the user, who selects the tags to attach to the post. We follow with additional details about each step.
We experimented with a number of methods for generating queries from a post, including using the entire text of the post and using links in it to locate cocitations. The best results were obtained by using a ``distinctive term'' query: standard corpus comparison methods are first used to derive the most distinctive terms in the vocabulary of a post (compared to the general vocabulary in the corpus); the top-ranking terms are in turn used as the query.
Two methods were used to evaluate the effectiveness of the tag suggestion methods. First, we manually examined the tags assigned to a random subset of 30 posts from our collection; for each tag, we decided whether the tag was indeed a relevant label for the post. This is the preferred method of evaluation, but due to its cost it can only be applied to a small number of posts; additionally, it is difficult to use non automated methods to tune and improve a system. Because of this, we used an automated method to evaluate a much larger subset of posts: AutoTag was used to tag 6000 of the ``tagged posts'' in our corpus - the posts which were assigned tags by their authors (we used only posts with 3 or more tags). Then, AutoTag's output was compared to the actual post tags. To account for minor differences in tags (``blogs'' and ``blogging''), we used string distance to compare the tags rather than exact string match. Even so, the automated precision scores are lower than manual ones, since tags which are useful for a post but were not originally used by its author are mistakenly taken to be incorrect. To demonstrate this, we evaluated the small test set with the automated method as well, resulting in substantially lower scores; this indicates that the actual performance of AutoTag on the large set is likely to be much better than reported by the automated evaluation.
For the manual evaluation, we measured precision at 10: the fraction of tags out of the top-10 suggestions by AutoTag which were judged as appropriate for the post by a human; only the first 10 results are checked because it is assumed that users are unwilling to examine long result lists. For the automated evaluation, we measured recall at 10 as well: the fraction of tags offered by AutoTag in the top-10 suggestions which were also assigned by the blogger out of the total number of tags assigned by her.
Table 1: Auto-tagging accuracy.
| Test Set | Evaluation | Precision@10 | Recall@10 |
| 30 posts | Automated | 0.38 | 0.47 |
| 30 posts | Manual | 0.59 | - |
| 6000 posts | Automated | 0.40 | 0.49 |
Results are shown in Table 1; an
example of tags offered by AutoTag is given in Table 2. On average, 4 to 6 suggestions out of
AutoTag's top-10 suggestions are either considered useful by the
blogger, or were actually used by her for the given post. Cursory
examination of posts for which AutoTag scores low shows many
non-English posts (for which much less data exists in the corpus,
entailing lower success of data-driven methods), and many tags
which are highly personal and used by few bloggers (such as names
of family members).
Table 2: Sample tags suggested for a post.
| http:/www.stillhq.com/diary/000959.html |
| On pitching products to bloggers Anil comments on how to pitch a product to him as a blogger, and makes good suggestions as Lindsay agrees before getting distracted by how this applies to press releases. I have to wonder though how much of this promotional pitching actually happens. I certainly haven.t ever had a product pitched to me for this site. I.ve had people pitch advertising, and other spammy things, but not products. Does it really happen to us normal people bloggers? |
| Suggested tags: PR, blogging, weblogs, marketing, net, gripes, email, small business life, Anil Dash, PR pitching |
| Original tags: blog, pitch, product, marketing, communications |
The different components of AutoTag provide fertile ground for further work: identifying effective ways to generate queries from a post and successful retrieval models to use; improving the aggregation of tags from the retrieved posts; and various methods for filtering and reranking the lists produced by AutoTag. In addition to the collaborative approaches described in this paper, we are currently investigating a ``local'' approach to tag suggestion, in which suggestions for tags are made without access to the entire blogosphere as is the case with AutoTag, but using deeper analysis of the contents of the post and the blog it belongs to.