A consumer-dependent (business-to-consumer) organization tends to present itself as possessing a set of human qualities, which is termed the brand personality of the company. The perception is impressed upon the consumer through the content, be it in the form of advertisement, blogs, or magazines, produced by the organization. A consistent brand will generate trust and retain customers over time as they develop an affinity toward regularity and common patterns. However, maintaining a consistent messaging tone for a brand has become more challenging with the virtual explosion in the amount of content that needs to be authored and pushed to the Internet to maintain an edge in the era of digital marketing. To understand the depth of the problem, we collect around 300K web page content from around 650 companies. We develop trait-specific classification models by considering the linguistic features of the content. The classifier automatically identifies the web articles that are not consistent with the mission and vision of a company and further helps us to discover the conditions under which the consistency cannot be maintained. To address the brand inconsistency issue, we then develop a sentence ranking system that outputs the top three sentences that need to be changed for making a web article more consistent with the company’s brand personality.

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Online medical forums have become a predominant platform for answering health-related information needs of consumers. However, with a significant rise in the number of queries and the limited availability of experts, it is necessary to automatically classify medical queries based on a consumer's intention, so that these questions may be directed to the right set of medical experts. Here, we develop a novel medical knowledge-aware BERT-based model (M BERT) that explicitly gives more weightage to medical conceptbearing words, and utilize domain-specific side information obtained from a popular medical knowledge base. We also contribute a multi-label dataset for the Medical Forum Question Classification (MFQC) task. M BERT achieves state-of-the-art performance on two benchmark datasets and performs very well in low resource settings. CCS CONCEPTS• Applied computing → Consumer health; • Computing methodologies → Supervised learning by classification.

With the ever-increasing need to manage and conserve the ecosystem, as well as the growing business potential for tourism, it is becoming increasingly important to address and align these two domains. Ecotourism enters the scene to provide a solution for anthropogenic interruptions at ecological tourist sites, with its potential to provide sustainable maintenance and development of both the environment and the local communities closely related with natural ecosystems. Tourism is one of the most valuable industries in India and the world, accounting for a considerable portion of most countries' economies. Chamoli is one of the tourist hotspots districts in India. Along with being an ecologically rich zone residing in the Himalayas, it possesses some of the highest peaks and national parks like Nanda Devi and valley of flowers. The study area is also prone to various natural disasters like floods, earthquakes, landslides, and the recent one being the rishi-ganga landslide of February 2021. The use of GIS tools in conjunction with AHP allows for a more streamlined and holistic approach to making scientifically calculated conclusions. We attempted to establish the prospective zones of ecotourism sites in our study region by considering a variety of factors that influence those sites of sustainable zones, such as slope, topographic roughness, elevation, road closeness, river proximity, and proximity to a protected area.  The study area's data and information were geospatially analysed to build an ecotourism potential map that can be used as a guide for planning sustainable resource management and development operations in the Chamoli district. Keywords: - Ecotourism (ET), Analytical Hierarchy Process (AHP), Site-Suitability, Chamoli.

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