Context-Sensitive Classification of Short Colloquial Text

Transcription

1 Context-Sensitive Classification of Short Colloquial Text TU Delft - Network Architectures and Services (NAS) 1/12

2 Outline Emotions propagate through a social network like viruses. Some people influence others opinions in online social networks 1. The data (Short Colloquial Text) 2. Sentiment Classification Objective vs. Subjective Compared to existing tools 3. Automatic Detection of Polarization Intensity 4. Networks of Concepts (Word-Graphs) 2/12

3 A Word about the used Dataset Twitter Twitter is the largest free source of user conversations at this time. Users post what they are doing, their opinions, asking questions or simply discuss whatever they want in 140 characters of text. Our dataset is based on the twitter stream, having a corpus of ca. 500,000,000 tweets. The Twitter sample stream provides: 1% off all Tweets -> randomly sampled (still 17 per second). The Text of a Tweet may contain meta information # as Topic in order to mentioned users RT indicates that this Tweet is a ReTweet Tweet Tweetid, location, created at, in reply to user, Text: I like the movie I saw last night but the cinema was bad. Userobject, If retweeted the full retweeted Tweet username, real name, timezone, Joined Twitter at, url, profile image url, description, Number of friends & followers location, language, number of posts 3/12

4 Sentiment Analysis Objective Subjective Classification Examples: I liked The King s Speech - subjective The King s Speech was a long movie objective I like you, even when watching The King s Speech - Subjective with a reference on you 4/12

5 Sentiment Analysis Objective Subjective Classification Reference detection (Stanford NLP [lexicographical parser]) : [I, liked, the, movie] (ROOT (S (NP (PRP I)) (VP (VBD liked) (NP (DT the) (NN movie))))) nsubj(liked-2, I-1), det(movie-4, the-3), dobj(liked-2, movie-4) I : nominal subject of liked, movie : direct object of liked Check if there is an adjective/verb referring to the subject of interest (WordNet or Stanford PoS [part-of-speech Tagger]). I/PRP liked/vbd the/dt movie/nn I is a personal pronoun, liked a verb in past tense, the a determiner and movie a noun. 5/12

6 Sentiment Analysis Classification Evaluation Subjectivity is mostly based on adjectives or verbs expressing the polarity related to the subject of the message. 1,073 randomly chosen English tweets related to movies of the 83rd Academy Awards(Oscars) Evaluation against: Manual Sentiment classification Twitter Sentiment SVM trained on tweets containing emoticons Tweet Sentiments SVM trained by users of the service Lingpipe SVM, Maximum Entropy, Naive Bayes trained on a given dataset IMDB / half of our hand classified dataset 6/12

7 Sentiment Analysis Classification Evaluation 7/12

8 Sentiment Analysis Polarity Classification 8/12

9 Sentiment Analysis Polarity Classification 9/12

10 Sentiment Analysis Polarity Classification Possible for all words /languages (252,000 words only from English tweets from the first 2 weeks in February 12): Sunday: Monday: Good, 0.19 Bad, Networking: 0.15 Sweet, 0.26 Sour, , , , , , , , , , , /12

11 Networks of Concepts Graphs generated by Word co-ocurrences Creating a graph of words: Words are connected if they appear in the same Tweet Links are directed and weighted The Link weight is given through the probability a word co-occurs with the second one. The node properties are given by term and document frequencies 11/12

12 Networks of Concepts Graphs generated by Word co-ocurrences 12/12

13 A Word about the used Dataset Twitter Sentiment Analysis Typical propagation pattern: (positive vs. negative Tweets) 13/12

14 Thank you for your attention Questions Delft University of Technology Faculty of Electr. Engineering Dept. of Telecommunication Mekelweg CD Delft The Netherlands Room: EWI /12