Sentiment analysis on Twitter has attracted much attention recently due to its wide
applications in both, commercial and public sectors. In this paper we present SentiCircles, a …

The volume of data on the social media is huge and even keeps increasing. The need for
efficient processing of this extensive information resulted in increasing research interest in …

Online product reviews contain valuable information regarding customer requirements
(CRs). Intelligent analysis of a large volume of online CRs attracts interest from researchers …

A huge volume of user-generated content is daily produced on social media. To facilitate
automatic language understanding, we study keyphrase prediction, distilling salient …

The latest advances in NLP (natural language processing) have led to the launch of the
much needed machine-driven toxic chat detection. Nevertheless, people continuously find …

The hash symbol, called a hashtag, is used to mark the keyword or topic in a tweet. It was
created organically by users as a way to categorize messages. Hashtags also provide …

Social media services such as Twitter generate phenomenal volume of content for most real-
world events on a daily basis. Digging through the noise and redundancy to understand the …

The increasing popularity of Twitter as social network tool for opinion expression as well as
information retrieval has resulted in the need to derive computational means to detect and …

Twitter has attracted millions of users to share and disseminate most up-to-date information,
resulting in large volumes of data produced everyday. However, many applications in …

Aspect extraction is one of the key tasks in sentiment analysis. In recent years, statistical
models have been used for the task. However, such models without any domain knowledge …