In the internet era, almost every business entity is trying to have its digital footprint in digital media and other social media platforms. For these entities, word of mouse is also very important. Particularly, this is quite crucial for the hospitality sector dealing with hotels, restaurants etc. Consumers do read other consumers reviews before making final decisions. This is where it becomes very important to understand which aspects are affecting most in the minds of the consumers while giving their ratings. The current study focuses on the consumer reviews of Indian hotels to extract aspects important for final ratings. The study involves gathering data using web scraping methods, analyzing the texts using Latent Dirichlet Allocation for topic extraction and sentiment analysis for aspect-specific sentiment mapping. Finally, it incorporates Random Forest to understand the importance of the aspects in predicting the final rating of a user.

Electric Vehicles (EVs) are a rapidly growing component of the automotive industry and are projected to have over 30 percent of the overall United States light duty vehicle market by 2030 (Wolinetz and Axsen, 2017). It's very different from traditional researches realated to transportation about road conditions (Huang et al., 2019), aviation (Bauranov et al., 2021) and manned driving (Chai et al., 2021). Furthermore, the US and other countries have bet big on Battery Electric Vehicles (BEVs), allotting funding for charging infrastructure, subsidies and tax credits and setting deadlines to phase out combustion engine vehicles. Correspondingly, the stock price of EV companies like Tesla have recently far exceeded those of traditional auto manufacturers, helping to illustrate the bullish outlook many consumers and investors have toward EVs in general. Despite this, there remain concerns among both consumers and experts about various aspects of electric cars, and despite the excitement surrounding them, EV adoption rates hovered around 1.8% in 2020 (energy.gov,

Researchers from Western University, SUNY Buffalo State College, University of Cincinnati, and City University of Hong Kong published a new paper in the Journal of Marketing that presents a methodological framework for managers to extract and monitor information related to products and their attributes from consumer reviews. Understanding how concrete product attributes form higher-level benefits for consumers can benefit various corporate teams. Concrete, or "engineered attributes" refer to technical specifications and product features. For example, in the context of tablet computers, such attributes include RAM, CPU, weight, and screen resolution. Understanding how combinations of these lower-level attributes form higher-level benefits, or "meta-attributes," for consumers, such as Hardware and Connectivity, can provide managers with actionable insights.

A new program can track all recalled foods based from Amazon customer reviews. Called BERT, the AI program identified thousands of recalled products with an accuracy rate of 74 percent. Researchers from the Boston University School of Medicine developed an artificial intelligence (AI) program that can detect unsafe food contaminated with chemicals, toxins, pathogens, and those which are mislabeled of allergens. Many people experience illness resulting from the consumption of unsafe food items, which is now considered a global health problem. Because of this, the researchers developed a machine learning approach to help detect reports of unsafe food items from Amazon, a multinational technology company and the world's largest online retailer.