132,378 research outputs found
Word Affect Intensities
Words often convey affect -- emotions, feelings, and attitudes. Lexicons of
word-affect association have applications in automatic emotion analysis and
natural language generation. However, existing lexicons indicate only coarse
categories of affect association. Here, for the first time, we create an affect
intensity lexicon with real-valued scores of association. We use a technique
called best-worst scaling that improves annotation consistency and obtains
reliable fine-grained scores. The lexicon includes terms common from both
general English and terms specific to social media communications. It has close
to 6,000 entries for four basic emotions. We will be adding entries for other
affect dimensions shortly
Sentiment Analysis
Recent advances in machine learning have led to computer systems that are human-like in behaviour. Sentiment analysis, the automatic determination of emotions in text, is allowing us to capitalize on substantial previously unattainable opportunities in commerce, public health, government policy, social sciences, and art. Further, analysis of emotions in text, from news to social media posts, is improving our understanding of not just how people convey emotions through language but also how emotions shape our behaviour. This article presents a sweeping overview of sentiment analysis research that includes: the origins of the field, the rich landscape of tasks, challenges, a survey of the methods and resources used, and applications. We also discuss discuss how, without careful fore-thought, sentiment analysis has the potential for harmful outcomes. We outline the latest lines of research in pursuit of fairness in sentiment analysis
Collective emotions online and their influence on community life
E-communities, social groups interacting online, have recently become an
object of interdisciplinary research. As with face-to-face meetings, Internet
exchanges may not only include factual information but also emotional
information - how participants feel about the subject discussed or other group
members. Emotions are known to be important in affecting interaction partners
in offline communication in many ways. Could emotions in Internet exchanges
affect others and systematically influence quantitative and qualitative aspects
of the trajectory of e-communities? The development of automatic sentiment
analysis has made large scale emotion detection and analysis possible using
text messages collected from the web. It is not clear if emotions in
e-communities primarily derive from individual group members' personalities or
if they result from intra-group interactions, and whether they influence group
activities. We show the collective character of affective phenomena on a large
scale as observed in 4 million posts downloaded from Blogs, Digg and BBC
forums. To test whether the emotions of a community member may influence the
emotions of others, posts were grouped into clusters of messages with similar
emotional valences. The frequency of long clusters was much higher than it
would be if emotions occurred at random. Distributions for cluster lengths can
be explained by preferential processes because conditional probabilities for
consecutive messages grow as a power law with cluster length. For BBC forum
threads, average discussion lengths were higher for larger values of absolute
average emotional valence in the first ten comments and the average amount of
emotion in messages fell during discussions. Our results prove that collective
emotional states can be created and modulated via Internet communication and
that emotional expressiveness is the fuel that sustains some e-communities.Comment: 23 pages including Supporting Information, accepted to PLoS ON
Analysis of the Impact of Vectorization Methods on Machine Learning-Based Sentiment Analysis of Tweets Regarding Readiness for Offline Learning
Twitter users use social media to express emotions about something, whether it is criticism or praise. Analyzing the opinions or sentiments in the tweets that Twitter users send can identify their emotions for a particular topic. This study aims to determine the impact of vectorization methods on public sentiment analysis regarding the readiness for offline learning in Indonesia during the Covid-19 pandemic. The authors labeled sentiment using two different approaches: manually and automatically using the NLP TextBlob library. We compared the vectorization method used by employing count vectorization, TF-IDF, and a combination of both. The feature vectors were then classified using three classification methods: naĂŻve Bayes, logistic regression, and k-nearest neighbor, for both manual and automatic labeling. To assess the performance of sentiment analysis models, we used accuracy, precision, recall, and F1-score for performance metrics. The best results showed that the Logistic regression classifier with the feature extraction technique that combines count vectorization and TF-IDF provided the best performance for both data with manual and automatic labeling
Fusing Audio, Textual and Visual Features for Sentiment Analysis of News Videos
This paper presents a novel approach to perform sentiment analysis of news
videos, based on the fusion of audio, textual and visual clues extracted from
their contents. The proposed approach aims at contributing to the
semiodiscoursive study regarding the construction of the ethos (identity) of
this media universe, which has become a central part of the modern-day lives of
millions of people. To achieve this goal, we apply state-of-the-art
computational methods for (1) automatic emotion recognition from facial
expressions, (2) extraction of modulations in the participants' speeches and
(3) sentiment analysis from the closed caption associated to the videos of
interest. More specifically, we compute features, such as, visual intensities
of recognized emotions, field sizes of participants, voicing probability, sound
loudness, speech fundamental frequencies and the sentiment scores (polarities)
from text sentences in the closed caption. Experimental results with a dataset
containing 520 annotated news videos from three Brazilian and one American
popular TV newscasts show that our approach achieves an accuracy of up to 84%
in the sentiments (tension levels) classification task, thus demonstrating its
high potential to be used by media analysts in several applications,
especially, in the journalistic domain.Comment: 5 pages, 1 figure, International AAAI Conference on Web and Social
Medi
IMPULSE moment-by-moment test:An implicit measure of affective responses to audiovisual televised or digital advertisements
IMPULSE is a novel method for detecting affective responses to dynamic audiovisual content. It is an implicit reaction time test that is carried out while an audiovisual clip (e.g., a television commercial) plays in the background and measures feelings that are congruent or incongruent with the content of the clip. The results of three experiments illustrate the following four advantages of IMPULSE over self-reported and biometric methods: (1) being less susceptible to typical confounds associated with explicit measures, (2) being easier to measure deep-seated and often nonconscious emotions, (3) being better able to detect a broad range of emotions and feelings, and (4) being more efficient to implement as an online method.Published versio
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