4,898 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
Affective Image Content Analysis: Two Decades Review and New Perspectives
Images can convey rich semantics and induce various emotions in viewers.
Recently, with the rapid advancement of emotional intelligence and the
explosive growth of visual data, extensive research efforts have been dedicated
to affective image content analysis (AICA). In this survey, we will
comprehensively review the development of AICA in the recent two decades,
especially focusing on the state-of-the-art methods with respect to three main
challenges -- the affective gap, perception subjectivity, and label noise and
absence. We begin with an introduction to the key emotion representation models
that have been widely employed in AICA and description of available datasets
for performing evaluation with quantitative comparison of label noise and
dataset bias. We then summarize and compare the representative approaches on
(1) emotion feature extraction, including both handcrafted and deep features,
(2) learning methods on dominant emotion recognition, personalized emotion
prediction, emotion distribution learning, and learning from noisy data or few
labels, and (3) AICA based applications. Finally, we discuss some challenges
and promising research directions in the future, such as image content and
context understanding, group emotion clustering, and viewer-image interaction.Comment: Accepted by IEEE TPAM
Emotion Detection for Afaan Oromo Using Deep Learning
Emotion detection in text has become more popular due to its various useful applications in a different area, such as tracking product perception, public opinion detection about political tendencies, stock market monitoring, text summarization, information extraction, recommendation system, question answering and etc. However, manually identifying emotion of a million of people and aggregating them towards a rapid and efficient decision is quite a challenging task due to the rapid growth of social media user. This study aimed to develop Afaan Oromo emotion detection model in order to tackle this challenge. This study adopts artificial neural network approach. We used python tools with Keras library. We conduct our experiments on five emotion class (anger(arii), love(jaalala), joy(gamachu), disgust(jibba), and sadness(gadda)) by collecting a total of 1005 emotional sentence of Afaan Oromo language that have been manually annotated. The sentence has been scraped from different official Facebook page such as Oromia Broadcasting Network (OBN) pages, Fana Broadcasting Corporation (FBC) Afaan Oromo page, and British Broadcasting Corporation (BBC) Afaan Oromo pages using Facepager tools by creating Facebook API id. After collecting these data all preprocessing steps like tokenization, stop word removal and normalization have been undertaken. We used word embedding’s for feature extraction of preprocessed data. Subsequently, we have applied three artificial neural network algorithms such as Feed forward neural network, long short-term Memory and Bidirectional long short-term memory for classification purpose of the vectorized sentence into their emotion class. We compared the three artificial neural network algorithms and found out that Bidirectional long short-term memory achieved the best performance. We have achieved an average accuracy of 66%, 78%, 83% using Feed Forward Neural Network, Long Short-Term Memory and Bidirectional Long Short-Term Memory respectively. Based on experimental result, the researcher concluded that increasing amount of dataset, tuning hyper parameters properly and trying by different algorithms can, in some case, improve the performance of the model. Keywords: Emotion Identification, Afaan Oromo, Artificial Neural Network, Social Media DOI: 10.7176/NMMC/92-01 Publication date:August 31st 202
Leveraging writing systems changes for deep learning based Chinese affective analysis
Affective analysis of social media text is in great demand. Online text written in Chinese communities often contains mixed scripts including major text written in Chinese, an ideograph-based writing system, and minor text using Latin letters, an alphabet-based writing system. This phenomenon is referred to as writing systems changes (WSCs). Past studies have shown that WSCs often reflect unfiltered immediate affections. However, the use of WSCs poses more challenges in Natural Language Processing tasks because WSCs can break the syntax of the major text. In this work, we present our work to use WSCs as an effective feature in a hybrid deep learning model with attention network. The WSCs scripts are first identified by their encoding range. Then, the document representation of the text is learned through a Long Short-Term Memory model and the minor text is learned by a separate Convolution Neural Network model. To further highlight the WSCs components, an attention mechanism is adopted to re-weight the feature vector before the classification layer. Experiments show that the proposed hybrid deep learning method which better incorporates WSCs features can further improve performance compared to the state-of-the-art classification models. The experimental result indicates that WSCs can serve as effective information in affective analysis of the social media text
Social media mental health analysis framework through applied computational approaches
Studies have shown that mental illness burdens not only public health and productivity but also established market economies throughout the world. However, mental disorders are difficult to diagnose and monitor through traditional methods, which heavily rely on interviews, questionnaires and surveys, resulting in high under-diagnosis and under-treatment rates. The increasing use of online social media, such as Facebook and Twitter, is now a common part of people’s everyday life. The continuous and real-time user-generated content often reflects feelings, opinions, social status and behaviours of individuals, creating an unprecedented wealth of person-specific information. With advances in data science, social media has already been increasingly employed in population health monitoring and more recently mental health applications to understand mental disorders as well as to develop online screening and intervention tools. However, existing research efforts are still in their infancy, primarily aimed at highlighting the potential of employing social media in mental health research. The majority of work is developed on ad hoc datasets and lacks a systematic research pipeline. [Continues.]</div
Fashion Conversation Data on Instagram
The fashion industry is establishing its presence on a number of
visual-centric social media like Instagram. This creates an interesting clash
as fashion brands that have traditionally practiced highly creative and
editorialized image marketing now have to engage with people on the platform
that epitomizes impromptu, realtime conversation. What kinds of fashion images
do brands and individuals share and what are the types of visual features that
attract likes and comments? In this research, we take both quantitative and
qualitative approaches to answer these questions. We analyze visual features of
fashion posts first via manual tagging and then via training on convolutional
neural networks. The classified images were examined across four types of
fashion brands: mega couture, small couture, designers, and high street. We
find that while product-only images make up the majority of fashion
conversation in terms of volume, body snaps and face images that portray
fashion items more naturally tend to receive a larger number of likes and
comments by the audience. Our findings bring insights into building an
automated tool for classifying or generating influential fashion information.
We make our novel dataset of {24,752} labeled images on fashion conversations,
containing visual and textual cues, available for the research community.Comment: 10 pages, 6 figures, This paper will be presented at ICWSM'1
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
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