25 research outputs found
A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applications
Sentiment analysis (SA) is an emerging field in text mining. It is the
process of computationally identifying and categorizing opinions expressed in a
piece of text over different social media platforms. Social media plays an
essential role in knowing the customer mindset towards a product, services, and
the latest market trends. Most organizations depend on the customer's response
and feedback to upgrade their offered products and services. SA or opinion
mining seems to be a promising research area for various domains. It plays a
vital role in analyzing big data generated daily in structured and unstructured
formats over the internet. This survey paper defines sentiment and its recent
research and development in different domains, including voice, images, videos,
and text. The challenges and opportunities of sentiment analysis are also
discussed in the paper.
\keywords{Sentiment Analysis, Machine Learning, Lexicon-based approach, Deep
Learning, Natural Language Processing
DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network
Unsupervised approaches for video anomaly detection may not perform as good
as supervised approaches. However, learning unknown types of anomalies using an
unsupervised approach is more practical than a supervised approach as
annotation is an extra burden. In this paper, we use isolation tree-based
unsupervised clustering to partition the deep feature space of the video
segments. The RGB- stream generates a pseudo anomaly score and the flow stream
generates a pseudo dynamicity score of a video segment. These scores are then
fused using a majority voting scheme to generate preliminary bags of positive
and negative segments. However, these bags may not be accurate as the scores
are generated only using the current segment which does not represent the
global behavior of a typical anomalous event. We then use a refinement strategy
based on a cross-branch feed-forward network designed using a popular I3D
network to refine both scores. The bags are then refined through a segment
re-mapping strategy. The intuition of adding the dynamicity score of a segment
with the anomaly score is to enhance the quality of the evidence. The method
has been evaluated on three popular video anomaly datasets, i.e., UCF-Crime,
CCTV-Fights, and UBI-Fights. Experimental results reveal that the proposed
framework achieves competitive accuracy as compared to the state-of-the-art
video anomaly detection methods.Comment: 10 pages, 8 figures, and 4 tables. (ACCEPTED AT WACV 2023