토픽 모델링

수학노트
둘러보기로 가기 검색하러 가기

노트

위키데이터

말뭉치

  1. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body.[1]
  2. The "topics" produced by topic modeling techniques are clusters of similar words.[1]
  3. Yang, Torget and Mihalcea applied topic modeling methods to newspapers from 1829–2008.[1]
  4. With this, we come to this end of tutorial on Topic Modeling.[2]
  5. Topic Modeling is a technique to extract the hidden topics from large volumes of text.[3]
  6. LDA’s approach to topic modeling is it considers each document as a collection of topics in a certain proportion.[3]
  7. We started with understanding what topic modeling can do.[3]
  8. with topic modeling, we can easily understand, organize, and summarize a large collection of textual information.[4]
  9. With this open-source tool, the repetitive process in topic modeling can be done at the click of a button or selection of an action bar without the need to do any coding.[4]
  10. In this guide, we’re going to take a look at two types of topic analysis techniques: topic modeling and topic classification.[5]
  11. Topic modeling is an ‘unsupervised’ machine learning technique, in other words, one that doesn’t require training.[5]
  12. First, we’ll delve into what topic modeling is, how it works, and how it compares to topic classification.[5]
  13. Topic modeling is a machine learning technique that automatically analyzes text data to determine cluster words for a set of documents.[5]
  14. As a result, the goal of topic modeling is to uncover these latent variables — topics — that shape the meaning of our document and corpus.[6]
  15. Topic modeling is a type of statistical modeling for discovering the abstract “topics” that occur in a collection of documents.[7]
  16. Depending on your choice of python notebook, you are going to need to install and load the following packages to perform topic modeling.[8]
  17. We’ve covered some cutting-edge topic modeling approaches in this post.[8]
  18. In topic modeling, we are mining a large collection of text.[9]
  19. What evidence do we have that search engine algorithms use topic modeling, apart from the obvious usefulness of topic modeling?[9]
  20. Select a topic modeling program.[9]
  21. Whether or not you utilize topic modeling in your search engine marketing campaign will depend largely on the level of sophistication of your search engine marketing team.[9]
  22. In our proposed method, clustering-based semantic similarity topic modeling is used in order to summarize the clinical reports based on latent Dirichlet allocation (LDA) in a MapReduce framework.[10]
  23. This paper investigates the topic modeling subject and its common application areas, methods, and tools.[11]
  24. Also, we examine and compare five frequently used topic modeling methods, as applied to short textual social data, to show their benefits practically in detecting important topics.[11]
  25. In this paper, we focused on the topic modeling (TM) task, which was described by Miriam (2012) as a method to find groups of words (topics) in a corpus of text.[11]
  26. In Section Proposed Topic Modeling Methodology, we focus on five TM methods proposed in our study besides our evaluation process and its results.[11]
  27. Topic modeling has been commonly used to discover topics from document collections.[12]
  28. W PMLR %X Topic modeling has been commonly used to discover topics from document collections.[12]
  29. Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data.[12]
  30. We wanted to highlight some from our most recent How to Use Topic Modeling to Extract Conversational Insights.[13]
  31. How is topic modeling different from categories themselves?[13]
  32. But in practice, you will likely combine topic modeling and classification models because the outcome from topic modeling is the input classification.[13]
  33. You can use classification to verify whether the topic modeling technique makes business sense.[13]
  34. As Figure 6.1 shows, we can use tidy text principles to approach topic modeling with the same set of tidy tools we’ve used throughout this book.[14]
  35. For example, we could collect a set of documents that definitely relate to four separate topics, then perform topic modeling to see whether the algorithm can correctly distinguish the four groups.[14]
  36. Each debate has multiple turns (a single uninterrupted speech by a unique congressperson), and we use each turn as a document for topic modeling.[15]
  37. This is to avoid experimental differences caused by the experimental group benefiting from exploring the corpus rather than from interactive topic modeling.[15]
  38. Using this information, we compute the variation of information (Meilă 2007) between the true labels and the topic modeling clusters.[15]
  39. This study provides evidence that the itm interface assists users in exploring a large corpus and that topic modeling is helpful for users attempting to understand legislative documents.[15]
  40. This essence of topic modeling strongly accords with biologists’ interests, which include discovering latent patterns in massive biological data.[16]
  41. Hence, in recent years, extensive studies have been conducted in the area of biological-data topic modeling.[16]
  42. As discussed in “Topic modeling” section the learning process of an LDA model is completely unsupervised; hence, its research area is currently concentrated on unlabeled data.[16]
  43. In this section, several examples of related articles will illustrate this kind of research, which predominates in the use of biological-data topic modeling.[16]

소스

메타데이터

위키데이터