49 research outputs found
Retail Resilience and Social Media Opinion Mining
This research focuses on the usefulness of social media opinion mining in the retail sector and what
constitutes an attractive high-street retail centre from the viewpoint of a consumer. Geo-located Twitter
data allows us to establish when, where and what people say about different retail centres. Comparing
this data with retail centres of differing vitality could allow us to draw conclusions about how useful
and predictive this source could be. Initial analysis revealed some contrasting text content within top
ranked and bottom ranked retail centres in Greater London
Social media and sentiment in bioenergy consultation
Purpose: The push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organisations towards energy development projects.
Design/methodology/approach: This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised, and illustrated using a sample of tweets containing the term ‘bioenergy’
Findings: Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable methodology, which bioenergy companies should be using to measure public opinion in the consultation process. Preliminary analysis shows promising results.
Research limitations/implications: Analysis is preliminary and based on a small dataset. It is intended only to illustrate the potential of sentiment analysis and not to draw general conclusions about the bioenergy sector.
Originality/value: Opinion mining, though established in marketing and political analysis, is not yet systematically applied as a planning consultation tool. This is a missed opportunity
On Identifying Disaster-Related Tweets: Matching-based or Learning-based?
Social media such as tweets are emerging as platforms contributing to
situational awareness during disasters. Information shared on Twitter by both
affected population (e.g., requesting assistance, warning) and those outside
the impact zone (e.g., providing assistance) would help first responders,
decision makers, and the public to understand the situation first-hand.
Effective use of such information requires timely selection and analysis of
tweets that are relevant to a particular disaster. Even though abundant tweets
are promising as a data source, it is challenging to automatically identify
relevant messages since tweet are short and unstructured, resulting to
unsatisfactory classification performance of conventional learning-based
approaches. Thus, we propose a simple yet effective algorithm to identify
relevant messages based on matching keywords and hashtags, and provide a
comparison between matching-based and learning-based approaches. To evaluate
the two approaches, we put them into a framework specifically proposed for
analyzing disaster-related tweets. Analysis results on eleven datasets with
various disaster types show that our technique provides relevant tweets of
higher quality and more interpretable results of sentiment analysis tasks when
compared to learning approach
Self-presentation and emotional contagion on Facebook: new experimental measures of profiles' emotional coherence
Social Networks allow users to self-present by sharing personal contents with
others which may add comments. Recent studies highlighted how the emotions
expressed in a post affect others' posts, eliciting a congruent emotion. So
far, no studies have yet investigated the emotional coherence between wall
posts and its comments. This research evaluated posts and comments mood of
Facebook profiles, analyzing their linguistic features, and a measure to assess
an excessive self-presentation was introduced. Two new experimental measures
were built, describing the emotional loading (positive and negative) of posts
and comments, and the mood correspondence between them was evaluated. The
profiles "empathy", the mood coherence between post and comments, was used to
investigate the relation between an excessive self-presentation and the
emotional coherence of a profile. Participants publish a higher average number
of posts with positive mood. To publish an emotional post corresponds to get
more likes, comments and receive a coherent mood of comments, confirming the
emotional contagion effect reported in literature. Finally, the more empathetic
profiles are characterized by an excessive self-presentation, having more
posts, and receiving more comments and likes. To publish emotional contents
appears to be functional to receive more comments and likes, fulfilling needs
of attention-seeking.Comment: Submitted to Complexit
Sentiment Analysis in Social Media Platforms: The Contribution of Social Relationships
The massive amount of data in social media platforms is a key source for companies to analyze customer sentiment and opinions. Many existing sentiment analysis approaches solely rely on textual contents of a sentence (e.g. words) for sentiment identification. Consequently, current sentiment analysis systems are ineffective for analyzing contents in social media because people may use non-standard language (e.g., abbreviations, misspellings, emoticons or multiple languages) in online platforms. Inspired by the attribution theory that is grounded in social psychology, we propose a sentiment analysis framework that considers the social relationships among users and contents. We conduct experiments to compare the proposed approach against the existing approaches on a dataset collected from Facebook. The results indicate that we can more accurately classify sentiment of sentences by utilizing social relationships