1 Masters Thesis Presentation By Debotosh Dey AUTOMATIC CONSTRUCTION OF HASHTAGS HIERARCHIES...
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Transcript of 1 Masters Thesis Presentation By Debotosh Dey AUTOMATIC CONSTRUCTION OF HASHTAGS HIERARCHIES...
![Page 1: 1 Masters Thesis Presentation By Debotosh Dey AUTOMATIC CONSTRUCTION OF HASHTAGS HIERARCHIES UNIVERSITAT ROVIRA I VIRGILI Tarragona, June 2015 Supervised.](https://reader036.fdocuments.us/reader036/viewer/2022062409/5697bfa41a28abf838c97175/html5/thumbnails/1.jpg)
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Masters Thesis Presentation
By Debotosh Dey
AUTOMATIC CONSTRUCTION OF HASHTAGS HIERARCHIES
UNIVERSITAT ROVIRA I VIRGILI
Tarragona, June 2015
Supervised by:Dr. Antonio Moreno
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Objectives
• Analyze and report the current state of the art on the analysis of tweets.
• Obtain a data set of tweets.• Develop, implement and test new mechanisms of
automatic hashtag hierarchy construction.– Use of co-occurrence frequency vs. use of semantic measures.
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What is Twitter
• Twitter is an online social networking service. • Each tweets is up to 140 characters.
– text– links– user mentions– symbols emoticons– hashtags
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Scope
• In general, tweets are usually ungrammatical. • Hashtags provide Twitter with a mechanism to semi-
structure its content. • Hashtags may be used to categorize sets of tweets.• Motivate the need for systems that can aggregate and
categorize all its content. • Examples:
– Large companies. – Governments.
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Why it is difficult ?
• Hashtags are unstructured. • Tweets are very terse, often lacking sufficient context to
categorize them. • Retrieval and classification methods have some basic
problems – Synonymy – Polysemy
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State of the Art
• The three basic kinds of techniques that have been proposed to detect the main topics of interest within a set of messages exchanged in a social network. – Probabilistic models. – Document-pivot approaches. – Feature-pivot methods.
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Methodology
• Clustering: this stage aims to group all the similar hashtags in clusters of related terms in order to detect topics of interest.
• Topic selection: general discussion about the detection of the most relevant classes.
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Some basic concepts and tools
• Twitter • Knowledge repositories
– WordNet – Ontology-based semantic similarity
• Techniques – Word-breaking – Clustering – Inter-class Homogeneity
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WordNet
• WordNet is the most commonly used online lexical and semantic repository for the English language.
• WordNet includes the main lexical categories (nouns, verbs, adjectives and adverbs) but ignore prepositions, determiners and other kinds of words.
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Ontology-based semantic similarity
• The science that aims to estimate the alikeness between words or concepts by evaluating their semantics.
• To calculating the semantic similarity between words we have used the Wu and Palmer distance function.
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Wu and Palmer distance function
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Word-breaking
• If a hashtags or a word does not match with a WordNet entry, the word-breaking technique is applied.
• It checks the matches between the subsequence of the hashtags and WordNet entries.
• If a match is found, the subsequence is stored. iPhone6 -> Phone , hone, one, onSmartPhone -> Smart, Phone, mart, art, hone, one
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Word-breaking
• Two(if possible) large non-overlapping sub-sequence are taken.
iPhone6 -> Phone SmartPhone -> Smart, Phone
• In English it is usual that the words on the left are adjectives or terms that denote a specialization of the main noun, located on the right. Therefore, this procedure finds the most general specialization present in WordNet.
• Thus when we analyze the data, we will consider “iPhone6”
as “Phone”.
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Clustering
• Clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups.
• we have chosen the hierarchical clustering method (with complete linkage) to classify the hashtags contained in a set of tweets
• Complete linkage calculates the distance between two clusters as the maximum distance between a pair of objects.
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Inter-class Homogeneity
• Inter-class Homogeneity is a concept related to the degree of similarity between elements in the same cluster or the measurement of the degree of homogeneity among population elements within the sampling clusters.
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Methodology : Clustering
• Syntactic hashtag clustering • Semantic hashtag clustering
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Syntactic hashtag clustering
• The main consideration of the similarity matrix is that the more frequently two hashtags appear in one tweets, the more related they are supposed to be.
• ∀i [1,n] j [1,n], c∈ ∀ ∈ ij = a (i,j) 𝑁𝑜𝑟𝑚 𝑙𝑖𝑧𝑒𝑑
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Semantic hashtag clustering
• Semantic similarity is calculated using the Wu & Palmer on WorNet.
• ∀i [1,n] j [1,n], sij = SemanticSimilarity (hi,hj) ∈ ∀ ∈
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Topic selection
• Three basic approaches:– Bottom-up approach. – Top-down approach. – Dendogram approach.
• Filtering has two threshold values:– Minimum number of elements. – Minimum inter-class homogeneity.
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Bottom-up approach
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Top-down approach
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Dendogram approach
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Case study :The Dataset
• 1000 tweets contained the hashtag #sensor
• Then for each hashtags (found in those 1000 tweets) we again extract, if possible, 100 tweets.
• 36646 hashtagged tweets with 19226 unique hashtags were collected.
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Analysis of the set of tweets: Cluster
Clustering based on Co-occurrence frequency
Clustering based on Semantic similarity
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• Threshold mHT (minimum number of hashtags in one cluster): – For co-occurrence: values ranging from 5 to 45 in interval of 5.– For semantic: values ranging from 5 to 50 in interval of 5.
• Threshold mHG (minimum inter-class homogeneity in one cluster):– For co-occurrence: values ranging from 0.1 to 0.65 in interval of
0.05.– For semantic: values ranging from 0.3 to 0.95 in interval of 0.05
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Analysis of the set of tweets
• Analysis 1: Total number of hashtags selected by the system
Total hashtags found for the Topic selection Bottom-Up (left) Top-Down (right) analysis Based on semantic similarity clustering
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Analysis of the set of tweets
• Analysis 1: Total number of hashtags selected by the system
Total hashtags found for the Topic selection Bottom-Up (left) Top-Down (right) analysis based on co-occurrence clustering
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Analysis of the set of tweets
• Analysis 2: Total number of clusters selected by the system
Total clusters found for the Topic selection Bottom-Up (left) Top-Down (right) analysis based on semantic similarity clustering
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Analysis of the set of tweets
• Analysis 2: Total number of clusters selected by the system
Total clusters found for the Topic selection Bottom-Up (left) Top-Down (right) analysis based on co-occurrence clustering
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Observations
• The clustering based on semantic similarity can extract more hashtags and clusters when we demand high homogeneity and high number of hashtags.
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Result : Semantic Clustering (Bottom Up)
Minimum hashtags 6, minimum inter-class homogeneity 0.9
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Result: Semantic Clustering (Top-Down)
Minimum hashtags 6, minimum inter-class homogeneity 0.9
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Result : Syntactic Clustering (Bottom-Up)
Minimum hashtags 6, minimum inter-class homogeneity 0.8
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Result : Syntactic Clustering (Top-Down)
Minimum hashtags 6, minimum inter-class homogeneity 0.8
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Observations
For semantic clustering Most of classes a general name can be set. The semantic centroid generated by the system is good. most precise clustering : higher “minimum homogeneity” and
lower “minimum number of hashtags”. System can generate a general class with a large number of
hashtags. For some clusters it is hard to set a name manually, but the system
can find a general semantic centroid.For co-occurrence clustering
For few classes a general name can be set. the semantic centroid generated by the system is not good System not able to generate a general class with a large number of hashtags.
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Dendogram Result
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Observations
• Each branch of the tree the semantic centroids go from general concepts to more specific ones.
• There are some long branches (e.g. entity, individual) that are not very illustrative.
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Conclusion
• A hierarchical clustering is applied to group all the similar hashtags.
• For the syntactic clustering: the co- occurrence matrix is normalized to calculate the similarity matrix.
• For the semantic hashtag clustering:– Wordnet– WordBreaking– Words not found in WordNet are removed– Similarity matrix is calculated using the application of
the Wu-Palmer distance on WordNet and co-occurrence frequency.
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Conclusion
• Bottom-up selection of clusters: Aims to find the most specific classes that fulfill the selection criteria.
• Top-down selection of clusters: Aims to find the most general classes that fulfill the selection criteria.
• Dendogram analysis of clusters: Aims to obtain a hierarchy of clusters that fulfill the
selection criteria.
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Conclusion
Regarding the case study– Number of hashtags and number of cluster: the
clustering based on semantic similarity is better. – Topic selection approaches: the clustering based on
semantic similarity is better. – Automatic construction of hashtags hierarchy based on
semantic analysis produces a better result.
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Future work
• Apply "stemming" techniques. • Concepts using other knowledge structures. e.g. YAGO
– Wikipedia (e.g., categories, redirects, infoboxes)– WordNet (e.g., synsets, hyponymy) – GeoNames
• The specific treatment of polysemic hashtags.
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THANK YOU……