Conventional topic modeling schemes, such as Latent Dirichlet Allocation, are known to
perform inadequately when applied to tweets, due to the sparsity of short documents. To …

Existing target-specific sentiment recognition methods consider only a single target per
tweet, and have been shown to miss nearly half of the actual targets mentioned. We present …

Arabic text sentiment analysis suffers from low accuracy due to Arabic-specific challenges
(eg, limited resources, morphological complexity, and dialects) and general linguistic issues …

One of the most important services for the users using the internet is the Micro blogging
which has evolved to be very important in today's life. A huge number of internet users share …

This paper describes the winning system for SemEval-2017 Task 6:# HashtagWars:
Learning a Sense of Humor. Humor detection has up until now been predominantly …

In this work, we present a new dataset for computational humor, specifically comparative
humor ranking, which attempts to eschew the ubiquitous binary approach to humor …

Sentiment Analysis has been looked into in an assortment of settings however in this paper,
the attention is RQ VXSSRVLWLRQ H [DPLQDWLRQ LQ 7ZLWWHU ZKLFK …

Hashtag segmentation, also known as hashtag decomposition, is a common step in
preprocessing pipelines for social media datasets. It usually precedes tasks such as …

This paper describes a system to answer a specific sentiment analysis problem described in
2015 iteration of SemEval (Semantic Evaluation series), Sentiment Analysis in Twitter as the …

The problem with distributed representations generated by neural networks is that the
meaning of the features is difficult to understand. We propose a new method that gives a …