# BiTTM: A Core Biterms-Based Topic Model for Targeted Analysis
## 4 Experimental Results
### Qualitative Evaluation
#### 4.5.2 Handling Semantically Approximate Targets  
When the targets supplied by users are semantically approximate, a set of similar relevant topics are supposed to be discovered. We further examine the performance of the comparative models in handling semantically approximate targets. In particular, we analyse two types of semantically approximate queries mentioned in Section 3.1: synonyms and diverse descriptions of the same event. An example of the first type is shown in Table 8. We query the dataset \"baby\" with two targets, \"bath\" and \"shower\", which share similar semantics in the data set of Amazon reviews of baby products. A successful model should return similar topics. As shown in the table, BiTTM is the only model that can obtain the set of four meaningful topics for both queries, while other methods either miss topics or generate vague content for topics. For instance, except BiTTM, the other three methods fail to identify the topic _blanket_ with respect to the query \"bath\", while TTM and BTM\({}^{*}\) can retrieve the topic with respect to the target \"shower\". According to the results of BiTTM, we find that _blanket_ is an important aspect of bath/shower, because most people will cover their babies with a blanket after a bath/shower. Hence, the topic _blanket_ is an aspect in which users are interested. Ignoring an important topic hinders downstream analysis and applications, such as high-quality personalised services and commodity recommended systems. As another example, there are two topics discovered by BiTTM only: _sentiment_ and _protection_. Checking the content of topic _sentiment_, we learn that users tend to associate emotional expressions (e.g., \"have a nice time with daughter/son\") when commenting on shower/bath products. This topic thus implies users' emotional polarity of products, which is important for applications such as user profiling, recommendation and public opinion monitoring. The topic _protection_ describes safety products that can be installed in tubs or on faucets. The safety issue of bath is an important concern especially for baby products, and it is non-ideal for the other three methods to ignore this topic. Moreover, we find that BTM-PD extracts only two topics for both queries and the content of the topics are too vague to understand (e.g., we are not able to assign semantic labels to the topics). There are six identical words between the two sets of top 10 words, which makes it very hard to distinguish between the semantics of the topics. The same  
situation occurs to BTM\({}^{*}\)--there are two similar topics about \"spout\". For example, given the query \"bath\", the two topics have eight identical words in the top 10 words. The content of these two topics may be correct, but the information expressed is redundant. It is not useful to generate identical topics but increasing the difficulty of further analysis. Table 9 shows an example of the second type. Given the dataset _Oscars_, both \"mistake\" and \"oscarsfail\" refer to the same event that the Best Picture Award, which should belong to _Moonlight_, was wrongly presented to _La La Land_ because of a wrong envelope. As we can see from the table, BiTTM can acquire three fine-grained relevant topics, which describes the process of the event development: At the beginning of the event, two guests present the Best Picture to _La La Land_, and no one was aware of the mistake. Many tweets emerge to talk about _La La Land_ and express congratulations to the actors and the producer, which can be seen from the content of the topic _beginning_. Next, the error is corrected and the real winner is another movie _Moonlight_. Topic _correction_ is a perfect interpretation of this stage. Note that, the top 10 words of this topic with respect to the query \"mistake\" contains the word \"oscarsfail\", which demonstrates the usefulness of the core biterms strategy used by BiTTM. The third topic _discussion_ covers the discussion of the actors' reaction after this mistake has happened.