Multimodal Sentiment Analysis in Natural Disaster Data on Social Media

Sefa Dursun, Süleyman Eken — Wed, 13 Nov 2024 00:00:00 +0000

INTRODUCTION: With the development of the Internet, users tend to express their opinions and emotions through text, visual and/or audio content. This has increased the interest in multimodal analysis methods.
OBJECTIVES: This study addresses multimodal sentiment analysis on tweets related to natural disasters by combining textual and visual embeddings.
METHODS: The use of textual representations together with the emotional expressions of the visual content provides a more comprehensive analysis. To investigate the impact of high-level visual and texual features, a three-layer neural network is used in the study, where the first two layers collect features from different modalities and the third layer is used to analyze sentiments.
RESULTS: According to experimental tests on our dataset, the highest performance values (77% Accuracy, 71% F1-score) are achieved by using the CLIP model in the image and the RoBERTa model in the text.
CONCLUSION: Such analyzes can be used in different application areas such as agencies, advertising, social/digital media content producers, humanitarian aid organizations and can provide important information in terms of social awareness.

Enhancing precision agriculture: An IoT-based smart monitoring system integrated LoRaWAN, ML and AR

Do Thanh Huong, Nguyen Thi Hang Duy, Pham Vu Minh Tu, Huu Hoang Hanh, Kou Yamada — Wed, 20 Nov 2024 00:00:00 +0000

Effective crop production and harvesting decisions rely on proper farm monitoring and management. Each region has distinct needs for farm oversight, but the primary focus remains on collecting and evaluating environmental data such as temperature, soil moisture, air humidity, all of which are vital to plant growth. Gathering this data on a large scale requires significant effort and is often based on intuition or simple measurement tools. This paper proposes a novel solution for farming data collection using an IoT platform integrated Long-Range Wide Area Networks (LoRaWAN) network application with Augmented Reality (AR) technology and Machine Learning (ML) algorithms to predict key environmental daily indexes. In a pilot study in Quang Tho, Vietnam, the system accurately predicted environmental conditions, reduced the risk of crop failure, and improved farm management efficiency. This approach enhances real-time data interaction and offers predictive analytics, supporting sustainable agriculture.

EAI Endorsed Transactions on Smart Cities

Multimodal Sentiment Analysis in Natural Disaster Data on Social Media

Enhancing precision agriculture: An IoT-based smart monitoring system integrated LoRaWAN, ML and AR