Intelligent Database Systems Lab Presenter: WU, MIN-CONG Authors: Abdelghani Bellaachia and Mohammed Al-Dhelaan 2012, WIIAT NE-Rank: A Novel Graph-based Keyphrase Extraction in Twitter

Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments 1

Intelligent Database Systems Lab Motivation When used in text to represent a lexical graph, it is possible to include a weight for the words that will measure the ranking more accurately instead of only relaying on the co-occurrence in Twitter. 2

Intelligent Database Systems Lab Objectives In task of extracted topical keyphrase, we start by proposing a novel unsupervised graph- based keyword ranking method, called NE-Rank, that considers word weights in addition to edge weights when calculating the ranking. 3

Intelligent Database Systems Lab Methodology-System Overview θ Twitter set topical subdatsets NE-Rank Hashtags Titles candidate keyphrase 4

Intelligent Database Systems Lab Methodology- Topic Extraction 5 For this paper

Intelligent Database Systems Lab Methodology- Topic Extraction Problem Top 5 Search TF-IDF Top 10 terms Insert 6

Intelligent Database Systems Lab 7 Methodology- Graph-based Keywords Ranking extant approach PageRank TextRank c c 6 Summary targetEdge weigetNode weiget PageRankwebnon-consideration TextRankwordconsiderationnon-consideration

Intelligent Database Systems Lab 8 Methodology- Graph-based Keywords Ranking proposing approach NE-Rank Summary targetEdge weigetNode weiget PageRankwebnon-consideration TextRankwordconsiderationnon-consideration NE-Rankwordconsideration

Intelligent Database Systems Lab 9 Methodology- Hashtags Titles Hashtags titles topical dataset word using an English dictionary with frequencies. Strengthening Strategy in-degree Boosted 5% extract split record

Intelligent Database Systems Lab Methodology- Candidate Keyphrase Generation positions keyphrase 1. magnment 2. business 3. customer 4. staff 5. finance Information magnment descending order find Twitter set 1. magnment 2. business 3. customer 4. staff 5. finance 10

Intelligent Database Systems Lab Methodology- Keyphrase Ranking keyphrases phrases list score filtering summarize hashtags Usage Another study is measuring sentiment in hashtags. Usage of hashtags as keywords annotation makes them of a very interest to our work. less than 5 times 11

Intelligent Database Systems Lab Experiment- Dataset and Preprocessing 12 tweetstokenshashtagsHashtags frequency Twitter set 31, ,1394,07940,674 Dataset Preprocessing remove non-english remove non-english Remove flag Ex: URL. emoticons. smileys transform slangs and abbreviation English dictionary Vocabulary OOV POS tagger removed stopwords LDA 500 iterations 30 topics

Intelligent Database Systems Lab Experiment- Evaluation Metrics 13 Precision Bpref

Intelligent Database Systems Lab Experiment- Results 14

Intelligent Database Systems Lab Experiment- Results 15

Intelligent Database Systems Lab Conclusions The potential and validity of both approaches have been demonstrated by conducting an experimental evaluation. 16

Intelligent Database Systems Lab Comments Advantages – keyphrase score not only rely on the co- occurrence. Applications – Automatic Keyphrase Extraction. 17