Detection of Natural Calamities from Assamese Posts in Social Media
نویسندگان
چکیده
منابع مشابه
Natural Calamities
Drought is perhaps the manifestation of desertification, which may be because of unprecedented soil erosion, large scale deforestation and abrupt change in micro-climate thereby increasing the temperature and reducing rainfall etc., ultimately leads to fall of groundwater level and hence, loss of agricultural productivity of the land, due to lack of water resources. Since, Indian Agriculture is...
متن کاملMachine Learning Based Detection of Clickbait Posts in Social Media
Clickbait (headlines) make use of misleading titles that hide critical information from or exaggerate the content on the landing target pages to entice clicks. As clickbaits often use eye-catching wording to attract viewers, target contents are often of low quality. Clickbaits are especially widespread on social media such as Twitter, adversely impacting user experience by causing immense dissa...
متن کاملImproving geolocation of social media posts
Pervasive social systems often take advantage of geographical information to provide real-time information to users based on their location. However, due to privacy concerns, many social media users do not share their exact geographical coordinates. In this paper, we describe our technique that predicts locations of posts that are not associated with explicit coordinates, a process called geolo...
متن کاملImpact of Context on Social Media Posts
This study examines the role of context in evaluating responses to social media posts online. Current sentiment analysis tools evaluate the content of posts without considering the broader context that the post comes from. Utilizing data from an in-person study, we examine differences between perceived sentiment evaluation when social media response posts are viewed in isolation and perceived s...
متن کاملMulticlass Emotion Analysis of Social Media Posts
We performed 15-class emotion classification on tweet-length social media posts. Our models were able to predict emotions with far greater accuracy than random chance. Among the models we built, the Linear SVM performed the best with 28.43% accuracy. Despite these results, however, distinguishing between similar emotions remains a challenge. The high variance in human text is hard to mitigate w...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IOP Conference Series: Materials Science and Engineering
سال: 2021
ISSN: 1757-899X
DOI: 10.1088/1757-899x/1070/1/012055