ABSTRACT:-
Visual Contents such as images and the video does not only contain objects, location, and actions but also cues about affect emotion and sentiment. Such information I very useful to understand visual content beyond semantic concept presence thus making it more explainable to the user. Images are the easiest medium through which people can express their emotions on social networking sites. Social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large-scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Significant progress has been made with this technology, however, there is little research focus on the picture sentiments. This paper proposes a novel approach that exploits latent correlations among multiple views: visual and textual views, and a sentiment view constructed using SentiWordNet.
INTRODUCTION:-
Sentiment analysis of online user-generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large-scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets so that prediction of sentiment from visual content is complementary to textual sentiment analysis. A picture is worth a thousand words. It is surely worth even more when it comes to convey human emotions and sentiments. Examples that support this are abundant: great captivating photos often contain rich emotional cues that help viewers easily connect with those photos. With the advent of social media, an increasing number of people start to use photos to express their joy, grudge, and boredom on social media platforms like Flickr and Instagram. Automatic inference of the emotion and sentiment information from such ever-growing, massive amounts of user-generated photos is of increasing importance to many applications in health-care, anthropology, communication studies, marketing, and many sub-areas within computer science such as computer vision. Think about this: Emotional wellness impacts several aspects of people’s lives. For example, it introduces self-empathy, giving an individual greater awareness of their feelings. It also improves one’s self-esteem and resilience, allowing them to bounce back with ease, from poor emotional health, and physical stress and difficulty.
Now Consider the example as shown in below figure 1.1. As people are increasingly using photos to record their daily lives, we can assess a person’s emotional wellness based on the emotion and sentiment inferred from her photos on social media platforms.
Visual Contents such as images and the video does not only contain objects, location, and actions but also cues about affect emotion and sentiment. Such information I very useful to understand visual content beyond semantic concept presence thus making it more explainable to the user. Images are the easiest medium through which people can express their emotions on social networking sites. Social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large-scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Significant progress has been made with this technology, however, there is little research focus on the picture sentiments. This paper proposes a novel approach that exploits latent correlations among multiple views: visual and textual views, and a sentiment view constructed using SentiWordNet.
INTRODUCTION:-
Sentiment analysis of online user-generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large-scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets so that prediction of sentiment from visual content is complementary to textual sentiment analysis. A picture is worth a thousand words. It is surely worth even more when it comes to convey human emotions and sentiments. Examples that support this are abundant: great captivating photos often contain rich emotional cues that help viewers easily connect with those photos. With the advent of social media, an increasing number of people start to use photos to express their joy, grudge, and boredom on social media platforms like Flickr and Instagram. Automatic inference of the emotion and sentiment information from such ever-growing, massive amounts of user-generated photos is of increasing importance to many applications in health-care, anthropology, communication studies, marketing, and many sub-areas within computer science such as computer vision. Think about this: Emotional wellness impacts several aspects of people’s lives. For example, it introduces self-empathy, giving an individual greater awareness of their feelings. It also improves one’s self-esteem and resilience, allowing them to bounce back with ease, from poor emotional health, and physical stress and difficulty.
Now Consider the example as shown in below figure 1.1. As people are increasingly using photos to record their daily lives, we can assess a person’s emotional wellness based on the emotion and sentiment inferred from her photos on social media platforms.
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