Abstract
Every day, a huge number of information is posted on social media platforms online. In a sense, the social media platform serves as a hybrid sensor system, where people communicate information through the system just like in a sensor world (we call it Social Sensor): they observe the events, and they report it. However, the information from the social sensors (like Facebook, Twitter, Instagram) typically is in the form of multimedia (text, image, video, etc.), thus coping with such information and mining useful knowledge from it will be an increasingly difficult task. In this paper, we first crawl social video data (text and video) from social sensor Twitter for a specific social event. Then the textual, acoustic, and visual features are extracted from these data. At last, we classify the social videos into subjective and objective videos by merging these different modality affective information. Preliminary experiments show that our proposed method is able to accurately classify the social sensor data’s subjectivity.
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Notes
- 1.
- 2.
In total, there are 36 tags from standard treebank POS tags (https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html), together with four additional tags specific for twitter: URL, USR for user, RT for retweet, HT for hashtag.
- 3.
The full list of Top 10 hashtags in the year of 2016 can be seen at https://blog.twitter.com/official/en_us/a/2016/thishappened-in-2016.html.
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Acknowledgments
This work was supported by the Natural Science Foundation of China (61672322, 61672324), the Natural Science Foundation of Shandong province (2016ZRE27468) and the Fundamental Research Funds of Shandong University (No. 2017HW001).
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Zhu, X., Gan, T., Song, X., Chen, Z. (2018). Sentiment Analysis for Social Sensor. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_86
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