This section presents how and what to mine from a news information tree (NIT). In Section 6.5.1, we propose to mine the favored or preferred news contents of a TV-station.
The news information tree can also be used to track the evolution of a series of news stories (see Section 6.5.2). In addition, the mining results from the NIT and the realtime
ratings can be combined to provide TV-news commercial buyers a very useful guidance.
6.5.1 Mine the news preference of a TV station
Generally speaking, a TV-station arranges the broadcasting sequence of each story in a news program according to their impact and attractiveness to audience. In fact, a preferred news story often gets more time on the air. By analyzing the sequence order and the length of stories, the preferred or the favored news stories of a TV-station can be roughly estimated or judged. Mining the NIT to extract favored or preferred types of news story from a TV station will help audience to find favor news channel.
The proposed news mining method is described as follows. Given N sets of keywords, K1, K2, ..., Ki, ..., KN, which correspond to N news topics (or subjects), let the following delta function δ(k, Ki) define the relations between a keyword k and a keyword set Ki:
δ(k, Ki) =
2. For each scene units sj, compute its association frequency F (Ki|sj) with respect to a subject Ki,
3. Compute the the association frequency of news program Fd(t|Ki) at time t and day d:
The associated frequency distribution from one segment of news program is not enough to represent the overall preference or trend of a news channel; thus long term statistics
is needed. By accumulating a longer period (say one month) of associated frequency of news subjects, the preference of a channel can be discovered. As shown in Fig. 6.14, keywords that are related to social news, political news, and entertainment news are applied to associate with and to accumulate frequency of news topics. As we can see in this example, the monitored news channel favors social and political news more than entertainment news.
6.5.2 The evolution of a series of news stories
The evolution of a news story can also be mined from the news information tree. By associating the keywords of a specific event with recorded news scenes over a period of days, then the accumulated association frequency of matched scene units presents an overall developing and progressing of the specific news stories. Figure 6.15 shows a sort of life-cycle of a particular news events. In addition, the spreading of the specific events to other areas, e.g., cities, counties, countries, etc., can also be retrieved from the associated names of locations in the matched scene units. For example, one can query a news story by using a particular people’s name, then the person’s daily schedule and/or whereabouts can be retrieved from the the recorded NIT.
6.5.3 The mining on TV commercial
Beside background stories, commercial records are also valuable information. Huang et al., [48] proposed commercial detecting and identifying methods in TV video clips. When a commercial frame contains image keywords in a video frame, video OCR techniques can be used to extract keywords to label the corresponding video clips. Otherwise, keyblock-based image retrieval methods [51] may be utilized to represent and to identify each commercial clips. However, manual annotation is needed to label the keyblock. By gathering statistical information of these labels and keywords in news programs, cross relationship between TV commercials, realtime ratings, and news stories can be observed and analyzed to achieve a useful marketing database. Two example areas, customer
modeling and cross-selling, in database marketing are discussed in the followings.
Customer modeling The basic idea behind customer (i.e., the commercial buyers and news audience) modeling is to improve audience response rates by targeting prospects that are predicted as most likely to respond to a particular advertisement or promotion. This is achieved by building a model to predict the likelihood that groups of news audience will respond based on news type, viewing time and news channels as well as previous viewing behavior. In addition, by targeting more effectively to prospects and existing commercial buyers, TV station operators can improve and strengthen customer relationships. The customer can perceives more value in TV news and commercials (i.e., both commercial buyers and news audience receive only products and/or services of interest to them).
Cross selling The basic idea behind cross selling is to leverage the existing customer base by selling them additional products (commercial time slots) and/or news services.
By analyzing the groups of products or services that are commonly purchased together and predicting each customer’s affinity towards different products using historical data, a TV-station can maximize its selling potential to the existing customers. Cross selling is one of the important areas in database marketing where predictive data mining techniques can be successfully applied. Using historical purchase data of different products from the customer database along with news type, viewing time and news channels, commercial buyers can identify their products that are most likely to be of interest to targeted news audience. Similarly, for each type of product (i.e., commercial or groups of commercials), a ranked list of different types of news or groups of audience, that are most likely to be attracted to that product. Then, arrangement of commercials with matched types of news to achieve a high likelihood of audience response rate.