Detecting Polarizing Language in Twitter using Topic Models and ML Algorithms

Gitari, Njagi Dennis; Zhang, Zuping

doi:10.14257/ijhit.2016.9.9.20

Cited by 2 publications

(1 citation statement)

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“…To the best of our knowledge there is no specific work tackling blame aspects extraction and classification in such a scenario via deep RNNs. Works by [1] closely relate to what we envision less for the methodology. Blame classification is subjectively related to sentiment analysis, an area that has been researched on for quite a while more so for a Twitter related perspective.…”

Section: Literature Reviewmentioning

confidence: 93%

Aspect of Blame in Tweets

Wandabwa

Naeem

Mirza

2017

Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion

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Twitter as an information dissemination tool has proved to be instrumental in generating user curated content in short spans of time. Tweeting usually occurs when reacting to events, speeches, about a service or product. This in some cases comes with its fair share of blame on varied aspects in reference to say an event. Our work in progress details how we plan to collect the informal texts, clean them and extract features for blame detection. We are interested in augmenting Recurrent Neural Networks (RNN) with selfdeveloped association rules in getting the most out of the data for training and evaluation. We aim to test the performance of our approach using human-induced terror-related tweets corpus. It is possible tailoring the model to fit natural disaster scenarios.

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Section: Literature Reviewmentioning

confidence: 93%