We live in a world of information overload. Manually
annotating text with topics isn't an option anymore. In this
paper, we deal with tweets. Firstly we recognize the
topics/entities they speak about. Having done that, we
cluster them based on the recognized entities and verbs to
get hierarchy of clusters. The clusters are then labelled
based on the most frequent entities.
● Twitter NLP
● Wiki Semantic Distance
● Verb net
To get first level of clusters:
1. Tokenize the tweets.
2. Apply POS tagging.
3. Apply IOB tagging on each token using Feature Extraction.
4. Extract Entities by applying some rules on the tweet with IOB token.
5. For the identified entity, find the nearest wikipedia entity using string edit
6. Create an inverted index based on the identified entities.
1. We use k-means clustering using jaccard similarity as the similarity metric
at each level.
2. We get the most frequent tags from each of the clusters and use them to
label the clusters.
Our methods successfully cluster tweets into a semantically related hierarchy.
We took a dataset that was constrained to a specific domain i.e. elections.
Future work may involve experimenting with different datasets. Wiki semantic
distance might be more useful in case of a more diverse dataset. Future work
can also focus on experimenting with different datasets to find out when wiki
semantic distance begins to significantly outperform jaccard similarity.
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