Sentiment Analysis of Tweets from Twitter
The project investigates the use of a multimodal feature learning approach, using neural network based models such as Skip-gram and Denoising Autoencoders, to address sentiment analysis of micro-blogging content, such as Twitter short messages, that are composed by a short text and, possibly, an image.
A novel architecture that incorporates neural networks is proposed and tested on several standard Twitter datasets, showing that the approach is efficient and obtains good classification results.
Focus: Methods or Design
Source: MICC
Readability: Expert
Type: Website Article
Open Source: No
External URL: https://www.micc.unifi.it/projects/advanced-web-applications/sentiment-analysis-of-tweets-from-twitter/
Keywords: N/A
Learn Tags: Data Collection/Data Set Design/Methods Framework AI and Machine Learning Research Centre
Summary: Overview and insights from a project that determines the sentiment analysis of Twitter data sets using a multimodal feature learning approach.