YouCat : Weakly Supervised Youtube Video Categorization System from Meta Data & User Comments using WordNet & Wikipedia
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The document presents YOUCAT, a weakly supervised system for categorizing YouTube videos using metadata and user comments without requiring labeled training data. It leverages WordNet for lexical knowledge and Wikipedia for named entity information to create genre definitions and seed lists that enhance genre identification performance. The results demonstrate that user comments provide valuable context for categorization, although they can also introduce noise, affecting classification accuracy.
YouCat : Weakly Supervised Youtube Video Categorization System from Meta Data & User Comments using WordNet & Wikipedia
- 1. YOUCAT : WEAKLY SUPERVISED YOUTUBE VIDEO CATEGORIZATION SYSTEM FROM META DATA & USER COMMENTS USING WORDNET & WIKIPEDIA Subhabrata Mukherjee1,2, Pushpak Bhattacharyya2 IBM Research Lab, India1 Dept. of Computer Science and Engineering, IIT Bombay2 24th International Conference on Computational Linguistics COLING 2012, IIT Bombay, Mumbai, Dec 8 - Dec 15, 2012
- 2. Motivation • In recent times, there has been an explosion in the number of online videos • Efficient query-based information retrieval has become very important for the multimedia content • For this, the genre or category identification of the video is essential • Genre identification has been traditionally posed as a supervised classification task
- 3. Supervised Classification Issues • A serious challenge for supervised classification in video categorization is the collection of manually labeled data (Filippova et. al, 2010; Wu et. al, 2010; Zanetti et. al, 2008) • Consider a video with a descriptor “It's the NBA's All-Mask Team!” • There must be a video in the training set with NBA in the video descriptor labeled with Sport, to identify its genre • With increasing number of genres and incorporation of new genre-related concepts, data requirement rises
- 4. Supervised Classification Issues Contd... • As new genres are introduced, labeled training data is required for the new genre • Very short text is provided by the user, for title and video description, which provide little information (Wu et. al, 2010) • Thus, video descriptors need to be expanded for better classification or else feature space will be sparse
- 5. Video Categorization Issues • Title is very short • Video descriptor is often very short or missing • User comments are very noisy • Too many slangs, abuses, off-topic conversations etc. • Requirement of a larger labeled training dataset
- 6. Novelty • All the surveyed works are supervised with a lot of training data requirement • YouCat does not have any labeled data requirement • New genres can be easily introduced • Use of WordNet and Wikipedia together has not been probed much for genre identification tasks
- 7. Questions • We try to address the following questions in this work: • Can a system without any labeled data requirement have comparable F-Score to a supervised system in genre identification tasks? • Can incorporation of user comments, which are typically noisy, improve classification performance? • Can the incorporation of lexical knowledge though WordNet and world knowledge through Wikipedia help in genre identification? • Is it possible for the system to achieve all these requirements with a minimum time complexity, which is essential for real-time inter-active systems?
- 8. YouCat : Features • Weakly supervised, requiring no labeled training data • Weak supervision arises out of • The usage of WordNet which is manually annotated • The specification of a set of 2-3 root words for each genre • Introduction of new genres does not require training data • Harvests features from Video Title, Meta-Description and User Comments • Uses WordNet for lexical knowledge and Wikipedia for named entity information • The genre identification algorithm has a time complexity of O(|W|), where |W| is the number of words in the video descriptor • Does not use user-provided genre information and co-watch data
- 10. Genre Definition • Genres are defined in the system beforehand with: • Genre Name – Example : Comedy • A set of Root Words (~ 2 – 3 words) for each genre which captures the characteristics of that genre – Example : Laugh, Funny Comedy Horror Romance Sport Technology comedy, funny, laugh horror, fear, scary romance, romantic sport, sports tech, technology, science Table 1. Root Words for Each Genre
- 11. Step 1: Seed List Creation for Each Genre • A Seed List of words is automatically created for each genre which captures all the key characteristics of that category. • Example - “love”, “hug”, “cuddle” etc. are the characteristics of the Romance genre • Root words of the genre are taken and all their synonyms are retrieved from a thesaurus • A thesaurus is used for this purpose which gives every day words and slangs • www.urbandictionary.com/thesaurus.php
- 12. Automatically Created Seed Word List from Thesaurus Comedy (25) Horror (37) Romance (21) Sports (35) Tech (42) funny, humor, hilarious, joke, comedy, roflmao, laugh, lol, rofl, roflmao, joke, giggle, haha, prank horror, curse, ghost, scary, zombie, terror, fear shock, evil, devil, creepy, monster, hell, blood, dead, demon love, romantic, dating, kiss, relationships, heart, hug, sex, cuddle, snug, smooch, crush, making out football, game, soccer, basketball, cheerleading, sports, baseball, FIFA, swimming, chess, cricket, shot internet, computers, apple, iPhone, phone, pc, laptop, mac, iPad, online, google, mac, laptop, XBOX, Yahoo Table 2. Seed Words for Each Genre
- 13. Step 2.1: Concept Hashing (WordNet) • Each word in WordNet is mapped to a genre using its synset and gloss of first sense • Example - dunk has the synsets - {dunk, dunk shot, stuff shot ; dunk, dip, souse, plunge, douse ; dunk ; dunk, dip}. • Gloss of the synset {dunk, dunk shot, stuff shot} is {a basketball shot in which the basketball is propelled downward into the basket} • The words in the synset of its most appropriate sense, from the context, should have been taken It requires WSD
- 14. Step 2.2: Concept Hashing (Wikipedia) • Named Entities are not present in the WordNet • Wikipedia is necessary for named entity expansion • All the named entities in Wikipedia with the top 2 line definition in their corresponding Wiki articles are retrieved. • Example: NBA is retrieved from the Wikipedia article as {The National Basketball Association (NBA) is the pre-eminent men's professional basketball league in North America. It consists of thirty franchised member clubs, of which twenty- nine are located in the United States and one in Canada.}. • A rough heuristic based on Capitalization is used to detect named entities (unigrams, bigrams, trigrams etc.)
- 15. Step 3: Concept List Creation
- 16. Step 3: Concept List Creation Contd… • Example : dunk (from WordNet) and NBA (from Wikipedia) will be classified to the Sports genre as they have the maximum matches (“shot”, “basketball”) from the seed list corresponding to the Sports genre in their expanded concept vector. • A concept list is created for each genre containing associated words in the WordNet and named entities in the Wikipedia
- 17. Video Descriptor Extraction • Given a video url the video title, meta description of the video and the user comments on the video from Youtube are retrieved • A stopwords list is used to remove words like is, are, been etc. • A lemmatizer is used to reduce each word to its base form • Thus “play”, “played”, “plays”, “playing” are reduced to its lemma “play” • A Feature Vector is formed with all the extracted, lemmatized words in the video descriptor
- 19. Feature Vector Classification Contd… • Weight assigned to any root word is maximum as it is specified, as part of the genre description, manually • Lesser weightage is given to words in seed list, as they are automatically extracted using a thesaurus • Weight assigned to concept list is the least to reduce the effect of topic drift during concept expansion • The topic drift occurs due to enlarged context window, during concept expansion, which may result in a match from seed list of some other genre
- 22. Algorithm for Genre Identification Pre-processing: 1.Define Genres and Root Words List for each genre 2.Create a Seed list for each genre by breadth-first-search in a Thesaurus, using root words in the genre or the genre name 3.Create a Concept List for each genre using all the words in WordNet (not present in Seed Lists) and Named Entities in Wikipedia using Equation 1 Input: Youtube Video Url 1.Extract Title, Meta Description of the video and User Comments from Youtube to form the video descriptor 2.Lemmatize all the words in the descriptor removing stop word. 3.Use Equations 2-4 for genre identification of the given video Output: Genre Tags Algorithm 1. Genre Identification of a Youtube Video Time Complexity O(|W|), |W| is the number of words in the video descriptor
- 25. Parameter Setting: Partially Supervised System
- 26. Data Collection • The following 5 genres are used for evaluation: Comedy, Horror, Sports, Romance and Technology • 12,837 videos are crawled from the Youtube following a similar approach like Song et al. (2009), Cui et al. (2010) and Wu et al. (2012) • Youtube has 15 pre-defined categories like Romance, Music, Sports, People, Comedy etc. • These are categorized in Youtube based on the user-provided tags while uploading the video • Videos are crawled from these categories and tags are verified
- 27. Data Collection Contd… • Only the 1st page of user comments is taken • Comments with length less than 150 characters in length are retained • Only those user comments are taken whose support is greater than some threshold • User comments are normalized by removing all the punctuations and reducing words like “loveeee” to “love” • The number of user comments varied from 0 to 800 for different videos
- 28. Data Collection Contd… Comedy Horror Sports Romance Tech Total 2682 2802 2577 2477 2299 12837 Comedy Horror Sports Romance Tech 226 186 118 233 245 Table 3. Number of Videos in Each Genre Table 4. Average User Comments for Each Genre
- 29. Baseline System • Multi-Class Support Vector Machines Classifier with various features, like combination of unigrams and bigrams, incorporating part-of-speech (POS) information, removing stop words, using lemmatization etc., is taken as the baseline Table 5: Multi-Class SVM Baseline with Different Features SVM Features F1- Score(%) All Unigrams 82.5116 Unigrams+Without stop words 83.5131 Unigrams+ Without stop words +Lemmatization 83.8131 Unigrams+Without stop words +Lemmatization+ POS Tags 83.8213 Top Unigrams+Without stop words +Lemmatization+POS Tags 84.0524 All Bigrams 74.2681 Unigrams+Bigrams+Without Stop Words+Lemmatization 84.3606
- 30. Discussions: Multi-class SVM Baseline • Ignoring stop words and lemmatization improves the accuracy of SVM • Related unigram features like laugh, laughed, laughing etc. are considered as a single entry laugh, which reduces sparsity of the feature space • POS info increases accuracy, due to crude word sense disambiguation • Consider the word haunt which has a noun sense - a frequently visited place and a verb sense - follow stealthily or recur constantly and spontaneously to; her ex-boyfriend stalked her; the ghost of her mother haunted her. • The second sense is related to the Horror genre which can only be differentiated using POS tags. • Top unigrams help in pruning the feature space and removing noise which helps in accuracy improvement • Using bigrams along with unigrams gives the highest accuracy
- 32. Single Genre Prediction : Effect of User Comments Table 6: Single Genre Identification with and without User Comments, without using Wikipedia & WordNet ROMANCE COMEDY HORROR SPORTS TECH
- 33. Discussions : Effect of User Comments • User comments introduce noise through off-topic conversations, spams, abuses etc. • Slangs, abbreviations and pragmatics in user posts make analysis difficult • Greater context provided by the user comments provide more clues about the genre • User information mostly helps in identifying funny videos and romantic videos to some extent • Horror videos undergo mild performance degradation
- 34. Single Genre Prediction : Effect of Concept Expansion Table 7: Single Genre Identification with and without User Comments, using Wikipedia &WordNet ROMANCE COMEDY HORROR SPORTS TECH
- 35. Discussions : Effect of Concept Expansion • In single genre prediction f1 score improvement of 3% (when user comments are not used) and 6% (when user comments are used) show that concept expansion is indeed helpful • External knowledge sources help in easy identification of new technological concepts. • Horror videos, again, undergo mild performance degradation • Performance improvement in Comedy using Wikipedia can be attributed to the identification of the concepts like Rotfl, Lolz, Lmfao etc.
- 36. Multiple Genre Prediction : Effect of User Comments using Concept Expansion Table 8: Multiple Genre Identification with and without User Comments, using Wikipedia &WordNet
- 38. Effect of Concept Expansion and User Comments on Single and Multiple Genre Prediction Genre Without User Comments + Without Wikipedia & WordNet With User Comments + Without Wikipedia & WordNet Precisi on Recall F1- Score Precisi on Recall F1- Score Romance 76.26 66.27 70.91 77.36 74.60 75.95 Comedy 43.96 40.00 41.89 69.23 66.00 67.58 Horror 80.47 68.67 74.10 76.45 70.33 73.26 Sports 84.21 68.71 75.67 85.07 69.94 76.77 Tech 90.83 73.50 81.25 84.09 78.45 81.17 Genre Without User Comments + With Wikipedia & WordNet With User Comments + With Wikipedia & WordNet Precisi on Recall F1- Score Precisi on Recall F1- Score Romance 76.06 70.63 73.24 80.16 78.57 79.36 Comedy 47.31 44.00 45.6 77.08 74.00 75.51 Horror 75.63 70.33 72.88 75.78 73.00 74.36 Sports 87.5 73.01 79.60 89.05 74.85 81.33 Tech 92.34 85.16 88.60 92.03 89.75 90.88 Genre Without User Comments + With Wikipedia & WordNet With User Comments + With Wikipedia & WordNet Precision Recall F1- Score Precisio n Recall F1- Score Romance 86.97 82.14 84.49 94.78 93.65 94.21 Comedy 77.5 73.67 75.54 91.78 89.33 90.54 Horror 83.92 80.00 81.91 89.56 88.67 89.11 Sports 96.38 81.6 88.38 96.38 81.6 88.38 Tech 96.63 92.34 94.44 98.56 92.03 95.18
- 39. Average Predicted Tags/Video in Each Genre Genre Average Tags/Video Without User Comments Average Tags/Video With User Comments Romance 1.45 1.55 Comedy 1.67 1.80 Horror 1.38 1.87 Sports 1.36 1.40 Tech 1.29 1.40 Average 1.43 1.60 Table 9: Average Predicted Tags/Video in Each genre • Mostly a single tag and in certain cases bi-tags are assigned to the video • Average number of tags/video increases with user comments • Greater contextual information available from user comments leading to genre overlap
- 40. Confusion Matrix Genre Romance Comedy Horror Sports Tech Romance 80.16 8.91 3.23 4.45 3.64 Comedy 3.13 77.08 3.47 9.03 7.29 Horror 10.03 9.34 75.78 3.46 1.38 Sports 0.70 7.30 0 89.05 2.92 Tech 0.72 5.07 0.36 1.81 92.03 Table 10: Confusion matrix for Single Genre Prediction
- 41. Discussions : Confusion Matrix • Romantic videos are frequently tagged as Comedy • Romantic movies or videos have light-hearted Comedy in them identifiable from the user comments • Horror videos are frequently confused to be Comedy, as users frequently find them funny and not very scary • Both Sports and Tech videos are sometimes tagged as Comedy • Bias towards Comedy often arises out of the off-topic conversation between the users in the posts from the jokes, teasing and mostly sarcastic comments etc. • Overall, from the precision figures, it seems that Sports and Tech videos are easy to distinguish from the remaining genres.
- 42. Comparison between Different Models Model Average F1 Score Multi-Class SVM Baseline: With User Comments 84.3606 Single Genre Prediction : Without User Comments + Without Wikipedia & WordNet 68.76 Single Genre Prediction : With User Comments + Without Wikipedia & WordNet 74.95 Single Genre Prediction : Without User Comments + With Wikipedia & WordNet 71.984 Single Genre Prediction : With User Comments+ With Wikipedia &WordNet 80.9 Multiple Genre Prediction : Without User Comments + With Wikipedia & WordNet 84.952 Multiple Genre Prediction : With User Comments + With Wikipedia & WordNet 91.48 Table 11: Average F1-Score of Different Models
- 43. Issues • Incorrect concept retrieval from Wikipedia due to ambiguous named entities • “Manchester rocks” can refer to Manchester United Football Club (Sports) or Manchester City (Place) • Considering only WordNet synsets gives less coverage. Considering the gloss information helps to some extent. • It runs the risk of incorporating noise. Consider the word good and the gloss of one of its synsets {dear, good, near -- with or in a close or intimate relationship}. • “good” is associated to Romance due to “relationship” • Uploader provided video meta-data is typically small • User comments provide information but incorporate noise as well • Auto-generated bot advertisements for products, off-topic conversation between users, fake urls and other spams • Mis-spelt words, different forms of slangs and abbreviations mar the accuracy. Example : “love” spelt as “luv”
- 44. •THANK YOU