40,532 research outputs found
Comparing and Combining Sentiment Analysis Methods
Several messages express opinions about events, products, and services,
political views or even their author's emotional state and mood. Sentiment
analysis has been used in several applications including analysis of the
repercussions of events in social networks, analysis of opinions about products
and services, and simply to better understand aspects of social communication
in Online Social Networks (OSNs). There are multiple methods for measuring
sentiments, including lexical-based approaches and supervised machine learning
methods. Despite the wide use and popularity of some methods, it is unclear
which method is better for identifying the polarity (i.e., positive or
negative) of a message as the current literature does not provide a method of
comparison among existing methods. Such a comparison is crucial for
understanding the potential limitations, advantages, and disadvantages of
popular methods in analyzing the content of OSNs messages. Our study aims at
filling this gap by presenting comparisons of eight popular sentiment analysis
methods in terms of coverage (i.e., the fraction of messages whose sentiment is
identified) and agreement (i.e., the fraction of identified sentiments that are
in tune with ground truth). We develop a new method that combines existing
approaches, providing the best coverage results and competitive agreement. We
also present a free Web service called iFeel, which provides an open API for
accessing and comparing results across different sentiment methods for a given
text.Comment: Proceedings of the first ACM conference on Online social networks
(2013) 27-3
Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious Disease Dynamics and Control
There is great interest in the dynamics of health behaviors in social
networks and how they affect collective public health outcomes, but measuring
population health behaviors over time and space requires substantial resources.
Here, we use publicly available data from 101,853 users of online social media
collected over a time period of almost six months to measure the
spatio-temporal sentiment towards a new vaccine. We validated our approach by
identifying a strong correlation between sentiments expressed online and CDC-
estimated vaccination rates by region. Analysis of the network of opinionated
users showed that information flows more often between users who share the same
sentiments - and less often between users who do not share the same sentiments
- than expected by chance alone. We also found that most communities are
dominated by either positive or negative sentiments towards the novel vaccine.
Simulations of infectious disease transmission show that if clusters of
negative vaccine sentiments lead to clusters of unprotected individuals, the
likelihood of disease outbreaks are greatly increased. Online social media
provide unprecedented access to data allowing for inexpensive and efficient
tools to identify target areas for intervention efforts and to evaluate their
effectiveness.Comment: Accepted for publication in PLoS Computational Biolog
Measuring the Influence and Intensity of Customer’s Sentiments in Facebook and Twitter
Organisations these days are actively using social media platforms to engage with potential and existing customers and monitor what they say about the organisation’s product or service. The most important area within social media monitoring lies in how to gain insight for sentiment analysis. Sentiment analysis helps in effective evaluation of customer’s sentiments in real time and takes on a special meaning in the context of online social networks like Twitter and Facebook, which collectively represent the largest online forum available for public opinion. Sentiment Analysis is not about retrieving and analyzing the analytics purely on the basis of positive, negative or neutral sentiment. It is imperative to assess the influencers of the sentiments in terms of Retweet and Share option used by them on Twitter and Facebook platform respectively. Measuring the intensity is other important aspect of sentiment analysis process. What kind of nouns, adjectives, verbs and adverbs are used in the opinion across the Twitter and Facebook platform matters as well since it exhibits the intensity of the underlying emotion in the text written. This study was conducted to propose a framework to identify and analyse the positive and negative sentiments present in Twitter and Facebook platforms and an algorithm was prepared to measure the intensity and influence of the positive, negative sentiment in particular using the document and sentence level analysis technique
Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control
There is great interest in the dynamics of health behaviors in social networks and how they affect collective public health outcomes, but measuring population health behaviors over time and space requires substantial resources. Here, we use publicly available data from 101,853 users of online social media collected over a time period of almost six months to measure the spatio-temporal sentiment towards a new vaccine. We validated our approach by identifying a strong correlation between sentiments expressed online and CDC-estimated vaccination rates by region. Analysis of the network of opinionated users showed that information flows more often between users who share the same sentiments - and less often between users who do not share the same sentiments - than expected by chance alone. We also found that most communities are dominated by either positive or negative sentiments towards the novel vaccine. Simulations of infectious disease transmission show that if clusters of negative vaccine sentiments lead to clusters of unprotected individuals, the likelihood of disease outbreaks is greatly increased. Online social media provide unprecedented access to data allowing for inexpensive and efficient tools to identify target areas for intervention efforts and to evaluate their effectiveness
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Structural balance emerges and explains performance in risky decision-making.
Polarization affects many forms of social organization. A key issue focuses on which affective relationships are prone to change and how their change relates to performance. In this study, we analyze a financial institutional over a two-year period that employed 66 day traders, focusing on links between changes in affective relations and trading performance. Traders' affective relations were inferred from their IMs (>2 million messages) and trading performance was measured from profit and loss statements (>1 million trades). Here, we find that triads of relationships, the building blocks of larger social structures, have a propensity towards affective balance, but one unbalanced configuration resists change. Further, balance is positively related to performance. Traders with balanced networks have the "hot hand", showing streaks of high performance. Research implications focus on how changes in polarization relate to performance and polarized states can depolarize
Multi-dimensional Conversation Analysis across Online Social Networks
With the advance of the Internet, ordinary users have created multiple
personal accounts on online social networks, and interactions among these
social network users have recently been tagged with location information. In
this work, we observe user interactions across two popular online social
networks, Facebook and Twitter, and analyze which factors lead to retweet/like
interactions for tweets/posts. In addition to the named entities, lexical
errors and expressed sentiments in these data items, we also consider the
impact of shared user locations on user interactions. In particular, we show
that geolocations of users can greatly affect which social network post/tweet
will be liked/ retweeted. We believe that the results of our analysis can help
researchers to understand which social network content will have better
visibility.Comment: Datasets will be anonymized and published at:
http://akcora.wordpress.com/2013/12/24/pointer-for-datasets
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