T.-K. Fan, C-H Chang/ Expert Systems with Applications 38(2011)1777- 1779 interests. From this survey, we could say that 80% of bloggers tend to recognize intention and detect sentiment for triggering-level to click the ads on their blogs and about 80% of ads click rates are interests. If no such targets are found, the system uses targets from related to personal interests and requirements. Thus, in this paper, the blogger's profile and searches the ad database to find the best we propose the idea of user-centric contextual advertising and use matching ads. These four modules: intention recognition, senti- the blogosphere as an example for realizing this idea. Compared ment detection, term expansion and target-ad matching are the with the traditional ad agency, which targets general visitors, blog- main components in our BCCA framework. The first two com ger-centric advertising considers bloggers themselves, as their ads nents analyze triggering-level interests, while the last two compo- targets and display ads based on bloggers'interest and intentions nents enhance the target-ad matching procedure. The connections as described below between modules are depicted in Fig. 2. Note that we place a priority on ad assignment based on differ 3. BCCA framework ent levels of interests. That is, triggering-level interests (i.e, short term interests) have higher priority than profile-level interests Traditional contextual advertising processes a given page a user (long-term interests). Thus, the sentiment detection module is in- visits to find related topics for matching ads, while blogger-Centric roked only when no positive intention is detected. If neither mod- ule detects intention or positive sentences, the system should then accordance with the blogger 's interests. Before demonstrating the use targets from the blogger's profile roposed framework, we will explain how bloggers'personal inter Next, the system proceeds to term expansion and the target-ad ests could be obtained. Generally speaking, an individual blog often matching agency. Due to the short form of ads and targets, we de- contains profile information, tags and posts, which we could clas- signed a term expansion component to enhance the likelihood of ify as indicating different levels of interest. intersection between targets and available ads. Finally, a retrieval function based on a query likelihood language model is deployed 3. 1. Profile-level for the target-ad matching strategy to rank the ads. the pseudo code for our ad assignment strategies is shown in Fig 3. Blog service providers (e.g. BlogSpot. com and Technorati. com) usually ask bloggers'to enter interests to build their profiles 3.3. Intention recognition (e.g, music, movies, reading, and other leisure pursuits) when they register as a member of the service. In addition to generic interests Given a triggering page, our aim in this section is to explain the collected at registration time, the tags on posts or the archive of process of recognizing whether there exist any intention-bearing past posts can be used to construct bloggers'profiles, showing their sentences. By modeling this problem one of classification, our job specific interests. Since these kinds of interest continue for a period here is the preparation of training sentences which must be labeled of time, we view them as long-term interests. In this paper, we as- as intentional or non-intentional. umed that each blog-site has an interest profile containing either generic or specific long-term interests. 3.3.1. Collecting data for classifier training Labeling each sentence as intentional or non-intentional is a 3. 2. Triggering-level time-consuming and costly task. In this study we propose a novel og posts are media in which bloggers express their opinions and interests, as well as their intensions. For example, the sen- tence, The Nokia N95 is a good cell phone for several reasons, "ex a blog post s the sentiments of the author toward the object in question, a Nokia N95. The target is not necessarily a named entity(e. g, the name of a person, location, or organization) but it can also be a Does this ncept (such as a type of technology ) a product name, or an ent. Finding such targets is one of the key components for tra- ditional contextual advertising. For Blogger-Centric Contextual Advertising, we argue that recognizing the intentions of authors ould be even more effective. For instance. consider the sentence Detection We re going to the doctor right now. "Fig. 1 indicates that the author has an immediate intention to see a doctor. As another example the sentence, " I am looking for a new laptop, " implies that the through Another consideration for blogger-centric advertising is whether the sentence presents negative sentiments. For example, the phrase, " canceling trip to Europe, " in Fig. 1 shows a negatively connotated target, which has a lower priority. As demonstrated in( Fan Chang, 2009), avoiding negative targets provides better contextual ads. Thus, we could say that their work is actually a Page-Ad pecial case of Blogger-Centric Contextual Advertising that aims Matching Collection at providing ads to the bloggers. our Blogger-Centric Contextual Advertising framework (BCCA), the advertising system analyzes the content of the page 3http:/trec.nist.gov. Fig. 2. The BCCA framework.interests. From this survey, we could say that 80% of bloggers tend to click the ads on their blogs and about 80% of ads’ click rates are related to personal interests and requirements. Thus, in this paper, we propose the idea of user-centric contextual advertising and use the blogosphere as an example for realizing this idea. Compared with the traditional ad agency, which targets general visitors, blog￾ger-centric advertising considers bloggers themselves, as their ads targets and display ads based on bloggers’ interest and intentions as described below. 3. BCCA framework Traditional contextual advertising processes a given page a user visits to find related topics for matching ads, while Blogger-Centric Contextual Advertising would assign ads to a given blog page in accordance with the blogger’s interests. Before demonstrating the proposed framework, we will explain how bloggers’ personal inter￾ests could be obtained. Generally speaking, an individual blog often contains profile information, tags and posts, which we could clas￾sify as indicating different levels of interest. 3.1. Profile-level Blog service providers (e.g., BlogSpot.com and Technorati.com) usually ask bloggers’ to enter interests to build their profiles (e.g., music, movies, reading, and other leisure pursuits) when they register as a member of the service. In addition to generic interests collected at registration time, the tags on posts or the archive of past posts can be used to construct bloggers’ profiles, showing their specific interests. Since these kinds of interest continue for a period of time, we view them as long-term interests. In this paper, we as￾sumed that each blog-site has an interest profile containing either generic or specific long-term interests. 3.2. Triggering-level Blog posts are media in which bloggers express their opinions and interests, as well as their intensions. For example, the sen￾tence, ‘‘The Nokia N95 is a good cell phone for several reasons,” ex￾press the sentiments of the author toward the object in question, a Nokia N95. The target is not necessarily a named entity (e.g., the name of a person, location, or organization) but it can also be a concept (such as a type of technology), a product name, or an event.3 Finding such targets is one of the key components for tra￾ditional contextual advertising. For Blogger-Centric Contextual Advertising, we argue that recognizing the intentions of authors could be even more effective. For instance, consider the sentence, ‘‘We’re going to the doctor right now.” Fig. 1 indicates that the author has an immediate intention to see a doctor. As another example, the sentence, ‘‘I am looking for a new laptop,” implies that the author probably will purchase a laptop. As such, targets are imme￾diate interests; ads centered around them might increase click￾through. Another consideration for blogger-centric advertising is whether the sentence presents negative sentiments. For example, the phrase, ‘‘canceling trip to Europe,” in Fig. 1 shows a negatively￾connotated target, which has a lower priority. As demonstrated in (Fan & Chang, 2009), avoiding negative targets provides better contextual ads. Thus, we could say that their work is actually a special case of Blogger-Centric Contextual Advertising that aims at providing ads to the bloggers. In our Blogger-Centric Contextual Advertising framework (BCCA), the advertising system analyzes the content of the page to recognize intention and detect sentiment for triggering-level interests. If no such targets are found, the system uses targets from the blogger’s profile and searches the ad database to find the best matching ads. These four modules: intention recognition, senti￾ment detection, term expansion and target-ad matching are the main components in our BCCA framework. The first two compo￾nents analyze triggering-level interests, while the last two compo￾nents enhance the target-ad matching procedure. The connections between modules are depicted in Fig. 2. Note that we place a priority on ad assignment based on differ￾ent levels of interests. That is, triggering-level interests (i.e., short￾term interests) have higher priority than profile-level interests (long-term interests). Thus, the sentiment detection module is in￾voked only when no positive intention is detected. If neither mod￾ule detects intention or positive sentences, the system should then use targets from the blogger’s profile. Next, the system proceeds to term expansion and the target-ad matching agency. Due to the short form of ads and targets, we de￾signed a term expansion component to enhance the likelihood of intersection between targets and available ads. Finally, a retrieval function based on a query likelihood language model is deployed for the target-ad matching strategy to rank the ads. The pseudo code for our ad assignment strategies is shown in Fig. 3. 3.3. Intention recognition Given a triggering page, our aim in this section is to explain the process of recognizing whether there exist any intention-bearing sentences. By modeling this problem one of classification, our job here is the preparation of training sentences which must be labeled as intentional or non-intentional. 3.3.1. Collecting data for classifier training Labeling each sentence as intentional or non-intentional is a time-consuming and costly task. In this study, we propose a novel A blog post Sentiment Detection Intention Recognition Page-Ad Matching A list of personal ads Term Expansion Does this post contain any sentences of positive intention Does this post contain any sentences of positive sentiment yes yes no no Profile Ad Collection Fig. 2. The BCCA framework. 3 http://trec.nist.gov. T.-K. Fan, C.-H. Chang / Expert Systems with Applications 38 (2011) 1777–1788 1779