## 2 Segmentation-Enhanced Topic Modeling for Automated Text Annotation
### Text Segmentation with TopicDiff-LDA
However, in a real-life setting of document labeling and classification, there would be little to no opportunity to pre- or re-train algorithms manually every time the processed data is updated. On the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Reference** & _Pk_ \\ \hline C99 & [43] & 0.105 \\ U00 & [46] & 0.078 \\ M09 & [47] & 0.027 * \\ TSM & [48] & 0.009 ** \\ GraphSeg & [49] & 0.066 \\ SegBot & [50] & 0.003 *** \\ TopicDiff-LDA & & 0.029 \\ \hline \hline \end{tabular}

* The algorithm requires pre-training on an external dataset. ** Requires gold-standard set -derived parameter setting. *** A supervised algorithm.

\end{table} 
Table 2: Segmentation results on the Choi dataset (unless noted otherwise, text segmentation in an unsupervised mode is assumed). Figure 3: Change in coherence score over the number of topics for the Choi dataset. other hand, the latter is a typical situation when dealing with online customer reviews-dynamic and noisy, semantically open-ended yet continually growing collections of texts accumulated by various e-commerce platforms. In [51], the authors presented an attempt to build a knowledge model for tourism recommender systems to automatically classify online reviews. It was found that, while providing for acceptable-quality results on average, LDA-driven single-label annotation often results in multi-topic reviews being misclassified. To address this problem, the TopicDiff-LDA algorithm can be applied, as it was proposed in Section 2. ### Training Data and The Knowledge Model Used

Online reviews written in English were crawled from an Indonesian tourism website ([[https://www.indonesia-tourism.com/forum](https://www.indonesia-tourism.com/forum)]([https://www.indonesia-tourism.com/forum](https://www.indonesia-tourism.com/forum)), last accessed on 31 January 2022). The reviews were manually filtered to exclude uninformative texts (i.e., with fewer than three sentences, written in languages other than English, etc.) A text document collection was thus created, as specified in Table 3.

After preprocessing, an LDA model was built on the review data with a total of 9 topics. The number of topics was decided by observing the coherence score computed for different LDA models but also taking into account general ontology considerations (for more detail, see [51]). By interpreting the top 10 most probable words in each topic, class labels were termed as follows (the topic top-10 tokens are listed after each label):

1. Historical Sites: museum, building, build, house, palace, dutch, mosque, time, collection, old. 2. Protected Area: forest, park, animal, national_park, species, bird, include, plant, conservation, type. 3. Natural Place: cave, river, location, district, road, hill, reach, tree, tourism, meter. 4. Temple: statue, build, stone, side, meter, wall, king, find, roof, base. 5. Mountain: mountain, mount, crater, sea_level, hill, high, peak, regency, view, scenery. 6. Beach: sea, fish, wave, boat, coast, small, beauty, white_sand, reach, sand. 7. General Information: park, travel, tour, want, facility, get, provide, activity, good, offer. 8. Things to Buy: market, batik, food, tourism, product, traditional, plantation, fruit, sell, find. 9. Cultural Heritage: traditional, dance, name, come, hold, become, call, ceremony, day, culture. Below, this is an example of a review from the processed collection:

_\"Watu Dodol Tourism Object in Banyuuangi is located in Kalipuro district, Banyuangi regency. The location is on Bypass Banyuangi to Situbondo. The distance from Banyuangi to Watudodol is 14 km, and from Ketapang port is only 5 km. Watudodol beach usually is full of local tourists for weekends or holidays. The visitors can enjoy the panoramic ocean or stroll to the hill located across the road. From the top of the hill, a beautiful panorama of the Bali strait can be seen. Culinary activities are another interesting thing to do here. Souvenirs made of shells and also stones are on sale in small shops.