Good and bad events: combining network-based event detection with sentiment analysis

Abstract

The huge volume and velocity of media content published on the Web presents a substantial challenge to human analysts. In prior work, we developed a system (network event detection, NED) to assist analysts by detecting events within high-volume news streams in real time. NED can process a heterogeneous stream of news articles or social media user posts, combining text mining and network analysis to detect breaking news stories and generate an easy-to-understand event summary. In this paper, we expand the NED event detection and summarisation approach in two ways. First, we introduce a new approach to named entity disambiguation for tweets, which contain minimal information due to brevity. Second, we apply sentiment analysis techniques to documents associated with a detected event to characterise the event as either broadly 'positive' or 'negative' based on media portrayal. Our expansion focuses on Twitter streams since Twitter has become an important news dissemination platform and is often the site where emerging events are first seen. To test the extended methodology, we apply it here to three data sets related to political elections in the UK and the USA. The addition of sentiment analysis to the NED event detection methodology improves the insight gained by the user by allowing quick evaluation of the perceived impact of an event. This approach may have potential applications in domains where public sentiment is relevant to decision-making around events, such as financial markets and politics.

Identification of affective valence of Twitter generated sentiments during the COVID-19 outbreak

“…This is because of the algorithms used by VADER which not only categorize the input text into positive or negative or neutral categories, but all compute the compound polarity scores to gain a fair idea about how positive or negative the sentiments are. VADER is applied not only for social network data but also on other data generated online such as emails (Borg and Boldt 2020 ) and this tool remains very popular, effective and contemporary (Dahal et al 2019 ; Moutidis and Williams 2020 ; Wei et al 2016 ). VADER has a lot of advantages - It works very well with social media content, movies, products and editorial reviews.…”

Twitter users’ coping behaviors during the COVID-19 lockdown: an analysis of tweets using mixed methods

“…Here polarity score is of type float and ranges between [−1.0 and +1.0]. VADER takes into consideration emojis, slangs, emoticons, degree modifiers and capitalizations for score calculation (Hutto and Gilbert, 2014; Becken et al , 2019; Borg and Boldt, 2020; Moutidis and Williams, 2020). Only Tweets in the English language were captured.…”

Socio-cultural changes influenced by information and communication technology

“…A study conducted by Di Wang, Ahmad Al-Rubaie, Benjamin Hirsch and Gregory Cameron Pole, for example, proposes a general system for population happiness index monitoring using sentiment analysis from a social media stream (Twitter) through comprehensive multi-level filters, which is of great relevance not only during the pandemic [9]. Emotion identification enables people to understand better the population sentiment toward particular events [10].…”